{"as_of":"2026-08-13T10:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:db133584aa506b317aca652b0fbb02eb22991cca64019ab01fac9293403305d4","coverage":[{"denominator":102,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T14:05:11.672665Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T19:16:17.173333Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-10T19:16:17.335293Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"cited_work":{"arxiv_id":"2411.15674","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.15674","snapshot_observed_at":"2026-08-10T19:16:17.335293Z","title":"Quantile deep learning models for multi-step ahead time series prediction","venue":"cs.LG","work_id":"b99818e0-f01a-45fa-9b55-ee06480e9904","year":2024},"citing_paper":{"arxiv_id":"2501.10337","last_updated":"2025-05-06T19:30:25Z","snapshot_observed_at":"2026-08-13T07:14:41.953260Z","submitted_at":"2025-01-17T18:21:25Z","title":"Uncertainty-Aware Digital Twins: Robust Model Predictive Control using Time-Series Deep Quantile Learning","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T19:16:17.173333Z"},"links":{"cited_paper":"/paper/2411.15674","citing_paper":"/paper/2501.10337"},"observation_digest":"sha256:6308a6080c29fe7ea67334061ea11e84b8cd0ea7c79e1c92f7ab991211061632","observation_id":"b5b4dc75-be8a-4fdc-a347-fa800714c8bb","resolution":{"observed_at":"2026-08-10T19:16:17.342482Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.15674/citation-record","integrity":"/paper/2411.15674/integrity","json":"/paper/2411.15674/citation-record.json","paper":"/paper/2411.15674"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.911738Z","title":"Koenker, G","venue":null,"work_id":null,"year":1978},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.911738Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:29b60684608f9a4d0dc034a6330d552c11ebc83ac98e78877c4360547c1b2ec8","observation_id":"b8e0ad99-d553-4bcf-8df7-f74ad83b09e4","resolution":{"observed_at":"2026-08-12T14:05:10.911738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1177/1471082x18759142","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.877639Z","title":"Waldmann, Quantile regression: A short story on how and why, Sta- tistical Modelling 18 (3-4) (2018) 203–218","venue":null,"work_id":"70b9ade6-8ab5-42ef-a6e9-93dedd4aa102","year":2018},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.918306Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:7aec6865ac2a1870fe112d7503fe52f790925993a2130f730470068ab7fb748d","observation_id":"abafd8bd-7abb-480c-8452-a2d18d67dd5d","resolution":{"observed_at":"2026-08-12T14:05:11.886141Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.930012Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.930012Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:12f38d41899dab77284ca68f3f228ded22e10459fd2aafa092518d92d39b66a6","observation_id":"def0c30b-0f94-4f93-90db-d1f6a79c8100","resolution":{"observed_at":"2026-08-12T14:05:10.930012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.938500Z","title":"Fitzenberger, R","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.938500Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:49a5d56075e54f6c94979470844f7e39f1a6b2cd3bed38440fe60b3ad62c1788","observation_id":"9c3389b7-04b0-429d-9ab6-a9c758ba88db","resolution":{"observed_at":"2026-08-12T14:05:10.938500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.949147Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.949147Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:083a889645bd87ded3ed9649429085939bc32d0c8ebb5b01200169d37b18a5e7","observation_id":"937373fe-25df-49e9-b8c6-473d07e5bfe4","resolution":{"observed_at":"2026-08-12T14:05:10.949147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.955402Z","title":"Briollais, G","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.955402Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:b1f17f170fb8ded975856f76704cc430079e40aaebe09517b79097b21b09c787","observation_id":"18fca6aa-0bc2-4232-aa25-f1f9797821c1","resolution":{"observed_at":"2026-08-12T14:05:10.955402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.965108Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.965108Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:060d8b492894ab2d6122dd51f93692566d9b7e4d53841da64e2265ad8a6ff260","observation_id":"28e8c1e7-9f16-4c88-b57c-ce8b5865352f","resolution":{"observed_at":"2026-08-12T14:05:10.965108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.974666Z","title":"Buchinsky, Changes in the us wage structure 1963-1987: Applica- tion of quantile regression, Econometrica: Journal of the Econometric Society (1994) 405–458","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.974666Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:407888a60a1a4bbc24c89f97def964e33be42159609ce14a8eedcf8d9ff38ccc","observation_id":"d0e2c35a-6413-4247-afa9-d36069936bbc","resolution":{"observed_at":"2026-08-12T14:05:10.974666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.980864Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.980864Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:15b1dd5bbf6f42614541dccb6cade0d103212d449741c199ce5c04cfbba97356","observation_id":"e834a445-85ac-4665-bbe7-4347144d3eae","resolution":{"observed_at":"2026-08-12T14:05:10.980864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.988039Z","title":null,"venue":null,"work_id":null,"year":1967},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.988039Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:c2e573f12a55e53c2f9dfe38a05157d2c39342f997f8b39602c02ce0a90e8581","observation_id":"a3f4085e-8570-4195-bc77-58870f2b893c","resolution":{"observed_at":"2026-08-12T14:05:10.988039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:10.995425Z","title":"Geladi, B","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:10.995425Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:c0a071282c01d7c93b50b2c1652b81aa66acaacd482be427c2bc1c4a89649e9b","observation_id":"f9a3f4ce-6531-4b02-894d-35daadb8daa3","resolution":{"observed_at":"2026-08-12T14:05:10.995425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.001223Z","title":"Vaysse, P","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.001223Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:3f483dedad7733b6d6e2127de70e2f8373bcfbca581dfcd845d176add0b18a14","observation_id":"34c0f858-9e66-4319-849e-afa164432e0f","resolution":{"observed_at":"2026-08-12T14:05:11.001223Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.007483Z","title":"Dogulu, P","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.007483Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:6a8edd519ad955b01446aba88153752f4e2487a4f359e746fa549a9c7fad791f","observation_id":"1423e7ce-33d9-43df-a220-c798b937b480","resolution":{"observed_at":"2026-08-12T14:05:11.007483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.013298Z","title":null,"venue":null,"work_id":null,"year":1973},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.013298Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:5c93fa3384cba0024400e9d3fc4a8efd14b8bdc8226c59b1026fb45764ab8a49","observation_id":"d48a7696-0351-4616-8de9-5472bce65045","resolution":{"observed_at":"2026-08-12T14:05:11.013298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.018636Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.018636Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:e5825711eb024d1d93c4f323f29e7872e434e27954331ffb5eacf47c8f0f88a4","observation_id":"feb88900-5d79-4755-ae7a-2ba38ce0041f","resolution":{"observed_at":"2026-08-12T14:05:11.018636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.025303Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.025303Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:1b813ea0c1ef13514c9111312f25a80a3feb8cc088c378dc8471cc34b2abe231","observation_id":"b401652c-8493-4562-8d52-f43be8521e3f","resolution":{"observed_at":"2026-08-12T14:05:11.025303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.041148Z","title":"Makkonen, Problems in the extreme value analysis, Structural safety 30 (5) (2008) 405–419","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.041148Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:88ed70dbe90cd932b55dba3bcc07f054cf782a03cdaf3504335c3da97b10e443","observation_id":"f05f52fd-a1db-4771-842b-bf0ed421745e","resolution":{"observed_at":"2026-08-12T14:05:11.041148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.050697Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.050697Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:88315742449fd3835605a364d93b817572de8a9211b5c1f80244860858ecfc28","observation_id":"1818896e-fd95-4472-a036-ba701fadb4bf","resolution":{"observed_at":"2026-08-12T14:05:11.050697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.056388Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.056388Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:91cb99d3ccecd478a94119e637841a101c37653647becc6eb888148ddde58aad","observation_id":"55f7054f-f6c8-4609-9e54-899f384d7627","resolution":{"observed_at":"2026-08-12T14:05:11.056388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.062868Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.062868Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:d91a9d49f328b10b277b8d0e646796044abe7135d8febb05749f1135fe7e8f9f","observation_id":"c78f319f-4113-4fbe-831d-a4b2eb33c2e5","resolution":{"observed_at":"2026-08-12T14:05:11.062868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.071468Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.071468Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:35e6ab761dbc037ecd60a11c7388a894a39300833ee115142f81a383849f135b","observation_id":"f521f166-cfbc-4aff-ad60-757302087172","resolution":{"observed_at":"2026-08-12T14:05:11.071468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.083443Z","title":"Cumperayot, R","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.083443Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:690a596f830764b3dfc9ac8ebffd99289d5a51bc25d919d78d72dd1d332fed9d","observation_id":"6f3f6e19-f345-4bae-9e99-993f82e2348f","resolution":{"observed_at":"2026-08-12T14:05:11.083443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.090670Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.090670Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:31d50b4a3ca2bdcd9a90a0acf6fad62868cdccac0ae4f830ecb68fad66f83002","observation_id":"1b8e4e63-c336-4b78-9ae6-2f446ad68041","resolution":{"observed_at":"2026-08-12T14:05:11.090670Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.097992Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.097992Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:d5dee9aad14e1eff44c5a5b2b4ff40fde3df788ada74f10ab7a39dfc9f1a8851","observation_id":"a4115dbf-8a06-46d4-9cb7-ecaaa07f9c4e","resolution":{"observed_at":"2026-08-12T14:05:11.097992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.724243Z","title":"Velthoen, C","venue":null,"work_id":"6ebcf7a0-6cad-4f38-bc70-8e6fe506df6a","year":2023},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.106097Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:9b2250720ef0cacbafc6882bc123cf8c9e2cea5521bc39dbbbc44a608e124a0e","observation_id":"31401706-1dc7-4d83-9382-8de6da0a4714","resolution":{"observed_at":"2026-08-12T14:05:14.732406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.693630Z","title":null,"venue":null,"work_id":"055b940d-2cf8-47af-ad1b-f5738b96b7ff","year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.113713Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:ae246a7d70bd8c1463d5fecfe4c7047d541ccf62ec871896b2a1b736596c3c63","observation_id":"6689e922-ddcc-485e-aaf6-7ced9fb42c62","resolution":{"observed_at":"2026-08-12T14:05:14.702677Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.120308Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.120308Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:a88988de7378d17b4c07ac1e702bf3dd4f232bd06eb8b57120aa47e5efa6ff7c","observation_id":"20072069-4331-407b-89bc-6fb839beab14","resolution":{"observed_at":"2026-08-12T14:05:11.120308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.650638Z","title":null,"venue":null,"work_id":"46fbcf8e-37b2-4189-9143-1f8ba94da3f4","year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.128817Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:1105885cedd3b2464b9aa9ade1d39dcffb693c8536b62185c323535d8e89b40e","observation_id":"b41e0348-ed67-41d0-b715-3c711ccd43a5","resolution":{"observed_at":"2026-08-12T14:05:14.660591Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.622721Z","title":null,"venue":null,"work_id":"193f144d-2c29-4ffd-b533-8fdc5f89250b","year":2020},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.135262Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:45513dd42b0b22cbad5e01ce54c520b2846fb3df04a2736605ee9858e2bd6ec0","observation_id":"929281ab-c7e7-4b87-acef-2e79bc66280b","resolution":{"observed_at":"2026-08-12T14:05:14.629327Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.600965Z","title":null,"venue":null,"work_id":"62a3aeee-f53d-47da-a491-15920d820286","year":2016},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.141944Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:fd3ab1deeb745a85ab8548b6a626f5a62b4cd2af729f4a291045f1dd90833586","observation_id":"78c0c2e6-f23e-4181-bcf6-f0261080b672","resolution":{"observed_at":"2026-08-12T14:05:14.609149Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.572283Z","title":null,"venue":null,"work_id":"b5a90c0d-b230-419c-8eac-048beea70cef","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.147301Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:94d96d7e7c0db52c42536958155c1d983b58a855512bd711dd5ddac7ab419928","observation_id":"46e47707-ea25-4911-b816-42a6b33e4778","resolution":{"observed_at":"2026-08-12T14:05:14.577733Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.10567","last_updated":"2025-08-02T19:09:34Z","snapshot_observed_at":"2026-08-13T09:49:53.860849Z","submitted_at":"2024-03-14T17:45:56Z","title":"Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products","version":3},"cited_work":{"arxiv_id":"2403.10567","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.10567","snapshot_observed_at":"2026-08-12T14:05:12.789824Z","title":"Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products","venue":"cs.LG","work_id":"bd951222-8534-4e1d-a7bb-1dd0c4461289","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.152831Z"},"links":{"cited_paper":"/paper/2403.10567","citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:2edaa449117e16336aba5fad699ec3c978e160863afb28d87304ed9b8a03d340","observation_id":"64367003-63f0-47fa-bae0-a60b90ac4057","resolution":{"observed_at":"2026-08-12T14:05:12.798670Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.549543Z","title":"Tyralis, G","venue":null,"work_id":"9c2c7982-110b-4ca1-839d-5422579f4686","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.160201Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:8c59d991fd93a803fa4d95a9cfa0c951c1da2ccc89e79bfa67360ce94cfc61e7","observation_id":"e7582791-4ade-4703-b53a-5fa818f231d2","resolution":{"observed_at":"2026-08-12T14:05:14.555018Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.526712Z","title":null,"venue":null,"work_id":"77ea22ba-3e95-4598-81c7-887028c45eb2","year":2022},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.169728Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:cd74d379937d40fed2adc5396c4d72cb3f2a94deb7bbc6f9861122691c30e840","observation_id":"98729132-35ff-401f-9f65-580b9df29a33","resolution":{"observed_at":"2026-08-12T14:05:14.532986Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.507205Z","title":null,"venue":null,"work_id":"e8d5a787-8b86-4085-94cf-0b83d8cbea3a","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.178209Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:93b0283f1f7d17897f24aab2f2c02b85957fb841c2aeb1796082a19ae9f91002","observation_id":"45b03a9b-b0dd-4118-bb15-78f76557135f","resolution":{"observed_at":"2026-08-12T14:05:14.513271Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.474915Z","title":null,"venue":null,"work_id":"5039efeb-937a-4f63-82ee-0f69de30c65e","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.186477Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:f2d447d5d80d48582287e72f6e4bd4d99807d35655b983c2681ce93e10d57aeb","observation_id":"f23912a8-f999-4b5c-a95b-2785376d4c44","resolution":{"observed_at":"2026-08-12T14:05:14.487065Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.445826Z","title":null,"venue":null,"work_id":"833f3085-ca91-491f-bf82-368e11d65832","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.195524Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:b1ae711ca935a7efb8c15a1a1ab878dfff5ac1933877d9a9b1000d7923f25ec9","observation_id":"58d9ccb2-bf0f-4e8a-9792-c7c6d0f814ac","resolution":{"observed_at":"2026-08-12T14:05:14.454541Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.415696Z","title":"Zhang, H","venue":null,"work_id":"b78519db-d5b2-41fb-aa9b-389c6ad0d8af","year":2018},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.204498Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:7e5a6d17f7b94990e2a63262428ffc90587f12bc00d5f7b71e4c707946c4951b","observation_id":"9c3daf18-18eb-4894-af3e-ec8785e8d702","resolution":{"observed_at":"2026-08-12T14:05:14.424934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.388447Z","title":null,"venue":null,"work_id":"0d4a8043-26a7-4a47-94b6-378731869d4e","year":2023},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.211228Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:8da42f9901e9d271595c4220e15e94e59d6e1414f0cd922d688e70f477cd1510","observation_id":"40a67409-6bab-457e-ad89-004e18254254","resolution":{"observed_at":"2026-08-12T14:05:14.399341Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.218259Z","title":"Tang, et al., Neural networks for partially linear quantile regression, Journal of Business & Economic Statistics (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.218259Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:135147a3a72f6d41c0aa512c43fbf708bb417104b21be61bbbfc91f9070ddb91","observation_id":"7e655e04-f4ff-44ee-b45b-da75ea32d182","resolution":{"observed_at":"2026-08-12T14:05:11.218259Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.354946Z","title":null,"venue":null,"work_id":"d67f2b3d-0171-4865-bf94-74b0617e56c1","year":2023},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.224372Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:e0cce4bf19fd42e775ffac4e4f1d6b2d66ed44ab2bf710e1c4d6e70d6e2c6302","observation_id":"0bb4a45c-14e4-4e8f-8082-9de2a4455243","resolution":{"observed_at":"2026-08-12T14:05:14.362849Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.317710Z","title":null,"venue":null,"work_id":"5d12086a-7c5e-49ac-8859-c06cd682a518","year":2018},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.230404Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:af40084f0a5138c4231737780266bb20a132f7747410b34df8987eef2418a27a","observation_id":"50088878-42e2-408b-ab7c-bc0d25a4006a","resolution":{"observed_at":"2026-08-12T14:05:14.327493Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1089/big","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:12.636173Z","title":null,"venue":null,"work_id":"54f64090-08f1-4e67-9445-5d680c66b7c9","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.236109Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:84a6e79407e836fb3fa0fce9f304664ba918c3eba46154dd9a94a425cc3d882e","observation_id":"44626b43-b022-4336-822b-5bc636a64a8f","resolution":{"observed_at":"2026-08-12T14:05:12.644011Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11431","last_updated":"2024-06-02T07:20:29Z","snapshot_observed_at":"2026-08-13T00:04:27.181898Z","submitted_at":"2024-05-19T03:15:27Z","title":"Review of deep learning models for crypto price prediction: implementation and evaluation","version":2},"cited_work":{"arxiv_id":"2405.11431","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.11431","snapshot_observed_at":"2026-08-12T14:05:12.527300Z","title":"Review of deep learning models for crypto price prediction: implementation and evaluation","venue":"cs.LG","work_id":"1acbe04b-78b7-42e6-9039-f68ac02f37ec","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.242861Z"},"links":{"cited_paper":"/paper/2405.11431","citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:9f6b8d4fae45a2474c4018ebd4e1d94089a2f62839e51b00f19dfb324c457b78","observation_id":"9aa65831-1ff3-4efc-bcca-74a2e6251cc6","resolution":{"observed_at":"2026-08-12T14:05:12.535640Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.249341Z","title":"Hochreiter, J","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.249341Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:344779aaeb8b5b53ec91ffcab3fa2eda2c5d4b62770f6d13f993ce46a420ee9e","observation_id":"11c987fa-1b73-460b-ad4b-24f5aa5a1ae6","resolution":{"observed_at":"2026-08-12T14:05:11.249341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.266109Z","title":"Alzubaidi, J","venue":null,"work_id":"796f261c-5da1-4942-8fe3-7c0e13d43864","year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.256782Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:8e8424e9f408f38ef9ce646f83aa6db7e69ce32b5edda3d298fe1f8491762109","observation_id":"371cce80-069a-41b3-9293-70d6fe930be8","resolution":{"observed_at":"2026-08-12T14:05:14.275801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.264636Z","title":"Chandra, S","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.264636Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:f4161084a3fd3b360539dbb13d5b330cf4cb6a52222284b29d8efb32af255a83","observation_id":"7b388615-0f85-4f15-87c0-d60300b12863","resolution":{"observed_at":"2026-08-12T14:05:11.264636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.195558Z","title":null,"venue":null,"work_id":"14e63ed0-370e-4c53-9280-1507d4cef3ca","year":2018},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.272744Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:5d4984b484b38f182e90d5826b016b24c3f8e4a0a6921851673b7c03011162cd","observation_id":"bbcc2f8c-74e8-41fc-a6d2-b73ca08c4000","resolution":{"observed_at":"2026-08-12T14:05:14.205494Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.159378Z","title":"Koenker, Quantile regression for longitudinal data, Journal of multi- variate analysis 91 (1) (2004) 74–89","venue":null,"work_id":"0070ab59-71b5-4de9-8fad-00f7c15cd095","year":2004},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.278605Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:e8ae1e8024f79c78c29a463436cf1b324e462e11b357710c11560def4f0dc9d8","observation_id":"c1c7c816-cfed-4f41-81b1-712bac7862c9","resolution":{"observed_at":"2026-08-12T14:05:14.169451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.128628Z","title":"Cai, Regression quantiles for time series, Econometric theory 18 (1) (2002) 169–192","venue":null,"work_id":"64480383-7749-43ee-989e-3c72aa53f305","year":2002},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.288084Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:7b39a99da932b3a02ed521542b93e1356a356f003aa3cb04aa2e49cd27925e85","observation_id":"e0ae0866-e89a-4d63-91d0-a6ff11f4c48c","resolution":{"observed_at":"2026-08-12T14:05:14.135352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.091270Z","title":"Xiao, Time series quantile regressions, in: Handbook of statistics, V ol","venue":null,"work_id":"63337a48-985f-403f-86ac-715aea78a985","year":2012},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.294260Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:e6b63708ee837029d9e35e9074172d305422338e23198bf85a42148c15b7d1b6","observation_id":"3c01e2e8-14d0-4472-aee6-d9886f13d5e7","resolution":{"observed_at":"2026-08-12T14:05:14.101200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.299865Z","title":"Koenker, Quantile regression: 40 years on, Annual review of economics 9 (2017) 155–176","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.299865Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:deb33a89005dac827b449b1c476e082ad779dbac8a3b71a6372a0cd2963a9cca","observation_id":"b808a379-91ea-411f-b6f1-388e945153ba","resolution":{"observed_at":"2026-08-12T14:05:11.299865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.068353Z","title":null,"venue":null,"work_id":"1925e532-2daa-4fac-bf52-d7a6781237e3","year":1999},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.305285Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:60b1946afe83ec32b5535d686b87af143b4ad74ae2d3926124f4fa6c2487ec59","observation_id":"a11b9c36-6e62-457c-ab1c-fe421edfb0b0","resolution":{"observed_at":"2026-08-12T14:05:14.076500Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.038342Z","title":"Pickands III, Statistical inference using extreme order statistics, The Annals of Statistics (1975) 119–131","venue":null,"work_id":"26ecccc6-9346-4b1e-aecc-cb75d92dafd2","year":1975},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.312213Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:7c9e69e5bd534b8e87ce22408ee1cd16e29f7c2499d7f057f61a5de5f6a7d0ed","observation_id":"4819c171-3ef4-418f-aff1-ec98c9896e91","resolution":{"observed_at":"2026-08-12T14:05:14.046940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:14.008305Z","title":null,"venue":null,"work_id":"f0a21042-9b14-4fa9-8667-b7f779a8112c","year":2003},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.323573Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:ef56d36655fe21e2c10662b7f7add4117025e4b3139ae5e644a2223227eef6a1","observation_id":"d86c56b6-637c-43e4-bba0-8c3701980beb","resolution":{"observed_at":"2026-08-12T14:05:14.016983Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.980347Z","title":"Castillo, A","venue":null,"work_id":"6e575af8-84b0-4fa7-80d3-64bf76c37aac","year":1997},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.334833Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:4ca68cf69025e1a3ae0c33fd6ed93875aabd200c42d6f51f1de459df4326a886","observation_id":"cb4b3198-e112-4acd-a351-2e355113d034","resolution":{"observed_at":"2026-08-12T14:05:13.986470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"stable/4355651","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:12.466442Z","title":"Ferreira, L","venue":null,"work_id":"bb9e6160-0d84-4e7c-bccf-e10a129f8e92","year":2015},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.342750Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:530266b457be1076bf8f0d8625fc715c7a979848f17b6fd171a5d49dbe463c27","observation_id":"5c582b78-2488-4928-9b3e-74e2ad289afd","resolution":{"observed_at":"2026-08-12T14:05:12.484920Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.946065Z","title":"Fr ´echet, Sur la loi de probabilit´e de l’´ecart maximum, Ann","venue":null,"work_id":"17a3728c-9717-45c6-ae43-a5a94e092e3e","year":1927},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.350228Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:8a8b9e947850f2f2c3f7bd2ae89a57f6591a6f8e4990dacb7dacbdff7c4562be","observation_id":"92fa1494-2932-48d3-8c7b-437583e81740","resolution":{"observed_at":"2026-08-12T14:05:13.955317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.919742Z","title":null,"venue":null,"work_id":"5b743f17-751f-486c-94a7-9931c64ddc29","year":1928},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.359597Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:f7e57e6c687a9a63256a59ea3b9a1809551687960496994cf7754ba97086df3c","observation_id":"f6263520-34bb-44ad-bf2b-101800838b27","resolution":{"observed_at":"2026-08-12T14:05:13.929935Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.897213Z","title":null,"venue":null,"work_id":"3f8b20ad-fb7a-44d5-9bfb-1e40ead07017","year":2014},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.370271Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:4e650e85256024ad70dbef8145872293869f4f68401b556f040085a3b558bc5c","observation_id":"5f6357b5-7cb8-41ac-add5-a39ab1eb1788","resolution":{"observed_at":"2026-08-12T14:05:13.903558Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/20-sts795.short","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.831241Z","title":"B ¨ucher, C","venue":null,"work_id":"42c942b6-1a1f-46dc-ba60-08a2b01301de","year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.377080Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:df4e66545a6e22e7a3bfb1102e77d33b8058a9245857fe0685f426c65129ff9a","observation_id":"50871bbc-93b4-4f1a-96c6-5461676608a8","resolution":{"observed_at":"2026-08-12T14:05:11.837444Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.869159Z","title":null,"venue":null,"work_id":"2d6c4a42-9622-4be8-b5b9-ec1f8db1c7f6","year":2015},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.382008Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:84ed20f788f47e5d6a0b9f343d2c0d22ba326d135e28f864ebbffc98ce40731a","observation_id":"84b78d09-5f99-4809-8cb6-d514557a0193","resolution":{"observed_at":"2026-08-12T14:05:13.876920Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.844567Z","title":null,"venue":null,"work_id":"3716a2a2-7c11-4a35-b794-7980546ad72a","year":2012},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.388695Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:64f0ac049a641fbc5fdbfc44b26d2c458fe943e25ddd95964a4518764a86b0db","observation_id":"366b8d32-3ba0-43b8-9f5d-195490a633c3","resolution":{"observed_at":"2026-08-12T14:05:13.851001Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.818424Z","title":null,"venue":null,"work_id":"eb79cc33-45aa-4f2e-9ca3-deab3bafbf05","year":2012},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.395529Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:2e395e537bb330c73babdb04a7beaf92403d05483c6449019cc4d2f416d9146d","observation_id":"19d462a5-d6c1-467a-afaf-b7d7996c0100","resolution":{"observed_at":"2026-08-12T14:05:13.829233Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.794059Z","title":null,"venue":null,"work_id":"a5666988-f4dc-4365-b6de-6cca00845396","year":2013},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.404160Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:bfa64a45ac0e014482c8c9f3186927f56d52796fb447300d218b9c36569fa7e0","observation_id":"1c8ba8a1-ccd4-4735-b6c3-c34d1f50cf70","resolution":{"observed_at":"2026-08-12T14:05:13.802635Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2012.22006","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:12.315248Z","title":null,"venue":null,"work_id":"bfe04be2-7ab4-4ddf-8105-196854dbd7d5","year":2012},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.410483Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:2920191f60f3703be1a0943f2fe7a45299c029328f0582916669ea5b272076d1","observation_id":"102e125b-d7ea-4612-ad02-2212e4a088ea","resolution":{"observed_at":"2026-08-12T14:05:12.337842Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15882","last_updated":"2025-02-11T04:41:10Z","snapshot_observed_at":"2026-08-12T23:17:58.689681Z","submitted_at":"2024-07-20T23:45:04Z","title":"Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments","version":2},"cited_work":{"arxiv_id":"2407.15882","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.15882","snapshot_observed_at":"2026-08-12T14:05:12.209596Z","title":"Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments","venue":"cs.LG","work_id":"76511c93-b2e6-4385-a1a9-5f5864618012","year":2024},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.419403Z"},"links":{"cited_paper":"/paper/2407.15882","citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:b49407ecde069ccfb8d077245cfe8fba7a43d621a585b94662a7ad510e9dfe5c","observation_id":"21ed7938-4fd9-48e0-b721-5b5808872cd2","resolution":{"observed_at":"2026-08-12T14:05:12.218054Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.765606Z","title":"Lampinen, A","venue":null,"work_id":"4944b62f-a398-4533-a841-7a4adfef19d8","year":2001},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.427106Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:c3e83eca789bff8ab1aa0cdb4ecd2398fbe1ad53f36b6d7933aa397f09791329","observation_id":"4d8bbfcc-ac34-433f-ab98-dad842a10f35","resolution":{"observed_at":"2026-08-12T14:05:13.776487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.732307Z","title":"Kononenko, Bayesian neural networks, Biological Cybernetics 61 (5) (1989) 361–370","venue":null,"work_id":"18f5e72a-cd58-4fa4-8788-4b7fad487d47","year":1989},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.436079Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:1fa49b258395409bc4af251ff40403362bd6017cf7fbc3f7d64276d84b6ec92c","observation_id":"0b96221a-8564-4fec-b518-2cf9ce93d635","resolution":{"observed_at":"2026-08-12T14:05:13.740507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.705319Z","title":null,"venue":null,"work_id":"9de67341-e5c3-4c91-b374-ff942d009edb","year":2017},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.443106Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:9ce2c33c1dfa6c18de09ce8b8186455e48efd6f7a912dc2073c5311146c628ea","observation_id":"f74b2a83-ce1b-4da6-aed5-60b1def53ed0","resolution":{"observed_at":"2026-08-12T14:05:13.714469Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.450768Z","title":"Chandra, Y","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.450768Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:5a0aee0578a6c013f476900c701f67cafdce9824f616ae12ff695d858536bca6","observation_id":"79628f46-91f3-43d6-944b-5ed72e485d23","resolution":{"observed_at":"2026-08-12T14:05:11.450768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.669447Z","title":null,"venue":null,"work_id":"37ddd27f-2faf-450d-80fc-1091aec45cd9","year":2023},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.457335Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:e117f0f8b1a662596ac4a306910065097e01d1f7cf20b6668cd118c139d23bd3","observation_id":"70cd61f8-ec54-4283-ae86-798930b80b14","resolution":{"observed_at":"2026-08-12T14:05:13.685258Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.462780Z","title":"Van Houdt, C","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.462780Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:21ad48abf699742ffe3240803fa22fe4372014a89264cfb33b9ce970a63eb08b","observation_id":"33f0cd00-6736-4f96-88de-2ee8a0130251","resolution":{"observed_at":"2026-08-12T14:05:11.462780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.640401Z","title":null,"venue":null,"work_id":"f71f2118-66b8-4beb-8720-ab682b954291","year":1990},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.468609Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:5099b76f71de2a12128f19e5e67d6adc2b800c3b750c685efaecfa4b53a2ca71","observation_id":"c29b6bde-3637-4eeb-969b-9ec0adfbb6cd","resolution":{"observed_at":"2026-08-12T14:05:13.647660Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.473894Z","title":null,"venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.473894Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:9b3bd0d07205950528aaf8e4e49586bd380e4706a5cfcd9b035f15710239a28d","observation_id":"a0aef1af-8eb4-4e0c-8335-79ac4bff906e","resolution":{"observed_at":"2026-08-12T14:05:11.473894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.580632Z","title":"Graves, J","venue":null,"work_id":"4b514d81-09fd-48c3-abdf-4f42d61c3792","year":2005},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.479245Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:8b41e579d374564a1c686123f08e394fec5991e2612b4a8bc4c3fcf39d2bbed0","observation_id":"cb0cd603-0bb5-4d4c-b067-9deea68a80b8","resolution":{"observed_at":"2026-08-12T14:05:13.588627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.556354Z","title":null,"venue":null,"work_id":"b6d7a4fc-05ac-4e34-8983-001959294cca","year":2019},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.485767Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:68aa48d760cdd8d1ecb53189d25878ca841fb62dd328b1812f0b73b37c09ed62","observation_id":"881b11ed-8c25-4693-bb68-b2e4657bbe8d","resolution":{"observed_at":"2026-08-12T14:05:13.564325Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.503345Z","title":"Sutskever, O","venue":null,"work_id":"45863235-7816-4238-9829-02d15f93aeb7","year":2014},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.492084Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:7589653f357c229c07a865176c5822aef7217381a5f88245e0c964d21db7e23f","observation_id":"315c0520-51b6-4e68-ac40-8454c9f7bf6f","resolution":{"observed_at":"2026-08-12T14:05:13.509676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.472754Z","title":null,"venue":null,"work_id":"bc2c8975-5ade-408b-8a26-3f422046bd11","year":2015},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.498009Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:7c5d75ce2ef3d9a955b825c66148fb95d76a1787cf824a1e52e3b8803f8b6dbe","observation_id":"a03e8486-fa88-48d6-ae5e-83dbbc66c3ff","resolution":{"observed_at":"2026-08-12T14:05:13.482431Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.446563Z","title":"Hecht-Nielsen, Theory of the backpropagation neural network, in: Neural Networks for Perception, Academic Press, 1992, pp","venue":null,"work_id":"fd4bf7b3-28af-4d29-9227-15664e0dd845","year":1992},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.508522Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:1e4e117a04b7c86c082bfb50a44c3c5b933cd5c377759e6d0c7b8dca3dac1c04","observation_id":"2762af75-9acf-4626-b42c-3b1105881a6f","resolution":{"observed_at":"2026-08-12T14:05:13.454433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.415162Z","title":"Hinaut, N","venue":null,"work_id":"1f3352e7-fbf9-473c-8c7b-ea9aa20db2ed","year":2021},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.518157Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:1bd8eec58526375ad88087f3c7274c0a2ff05a3f8d82a675f6b6e082a0ae0d4c","observation_id":"711726ac-5be7-4048-9849-ccc89eee6d01","resolution":{"observed_at":"2026-08-12T14:05:13.422307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.393029Z","title":null,"venue":null,"work_id":"e0224cb9-1f35-4a45-8604-507289bdccaf","year":1977},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.525393Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:53401722e588f09a196d188b5b6949a6514ed354d4bb6e7beaa3ffc78e95970b","observation_id":"417d65c5-aa7c-4f43-9c7a-291fb69c9e74","resolution":{"observed_at":"2026-08-12T14:05:13.400510Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.532997Z","title":null,"venue":null,"work_id":null,"year":1960},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.532997Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:259ce37a28c0963aa6935f35546f6407ea694fd677f2afc4ed919c0ec1819cb0","observation_id":"d8f4c472-9b6d-444a-a0d0-8ab9f6d5da63","resolution":{"observed_at":"2026-08-12T14:05:11.532997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-12T14:05:11.558270Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.558270Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:c70b8f591249d905dc38bd25ca8650614f5f014161798d759ea1c7d1603a4b0e","observation_id":"4dacc4e9-dc00-4463-80bd-d5371f51ba28","resolution":{"observed_at":"2026-08-12T14:05:11.558270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.346861Z","title":"Greaves, B","venue":null,"work_id":"9ed1fc03-9db4-4c50-82d2-b31475f78558","year":2015},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.565559Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:b8f74969cd1a0a0eb1c05a06341afe553cdf70809d7ba290d5c1f635a44f4596","observation_id":"9cbebfe5-ca27-4e05-8aaf-a12061448f54","resolution":{"observed_at":"2026-08-12T14:05:13.356998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.304236Z","title":null,"venue":null,"work_id":"e2084289-a40a-4011-8518-0f2aa3a46b16","year":2005},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.570543Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:1dc7483fc41f8b95547ee40c27d27e12d397a32bf25ab68d010f7e0821938d76","observation_id":"50adff2f-9cbe-45f5-93c3-652102266afc","resolution":{"observed_at":"2026-08-12T14:05:13.314189Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.277777Z","title":null,"venue":null,"work_id":"eebbd7ad-9b29-4e34-a045-3362d99b034e","year":2014},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.577554Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:2cd7193f6f4c9cd47b8274bc7e692dba23c2b4e707c6f8c339d733e68aa8b05e","observation_id":"a2793440-c499-4bab-a047-01759d3933af","resolution":{"observed_at":"2026-08-12T14:05:13.287390Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.244981Z","title":null,"venue":null,"work_id":"616d1efb-faaa-4dcd-ac1b-9e61f3356f17","year":1950},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.584694Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:f2f4cfa322282ded0e55d2178724ac56fe335263400a478727e95f543c00a779","observation_id":"f96f71b9-7c6b-44e4-bfdc-d5ca8f8a5c55","resolution":{"observed_at":"2026-08-12T14:05:13.256519Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.211612Z","title":null,"venue":null,"work_id":"5604cbb2-789c-47c9-8e68-543d5ed898e7","year":2009},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.594217Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:f0197ac9eea3eac2581a57962d3b6cb73c72020188dc7c09d5251a4370096cb2","observation_id":"0bb8c466-a208-4754-a5ed-6e5652b76b24","resolution":{"observed_at":"2026-08-12T14:05:13.221611Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.183102Z","title":null,"venue":null,"work_id":"bdbe0843-b624-479e-8420-fb46e8abf6a7","year":2020},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.600145Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:c53643ea9de0de4300f551e866e5761bab388d7945f7a9bb06027022d7dd1f5f","observation_id":"5ad9f9cc-a631-4b7a-99ba-3c2d142b024e","resolution":{"observed_at":"2026-08-12T14:05:13.193357Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.150031Z","title":"Muthukrishnan, R","venue":null,"work_id":"7d78329b-94ff-4c46-a338-226ed031d502","year":2016},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.607301Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:0496c01eff24293864a5ea8b7c9a69064e6fc3a1692062870f97d367a9c667c0","observation_id":"ef74165d-90b1-41d8-a2b8-07cef8297311","resolution":{"observed_at":"2026-08-12T14:05:13.158841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.09169","last_updated":"2023-06-27T19:36:14Z","snapshot_observed_at":"2026-08-10T12:14:13.826563Z","submitted_at":"2015-09-30T13:38:31Z","title":"Lecture notes on ridge regression","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.09169","snapshot_observed_at":"2026-08-12T14:05:11.615152Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.615152Z"},"links":{"cited_paper":"/paper/1509.09169","citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:d0e45335f7d13c698a7dff0d7ef100557952b2905c785d3048438528bd030144","observation_id":"e7fed99a-8d9e-4897-8ffe-2deefa32b641","resolution":{"observed_at":"2026-08-12T14:05:11.615152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.110005Z","title":"Wager, S","venue":null,"work_id":"80b94131-aedb-4f4c-a578-9ed2dd50d778","year":2013},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.620657Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:85b7b93d4bad3b894b6800f12fcb7652b0ffb5b640efded1a59e87db4c9f3563","observation_id":"7241811d-5187-467b-b7a6-af6373a4de66","resolution":{"observed_at":"2026-08-12T14:05:13.129277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.081189Z","title":null,"venue":null,"work_id":"d44f33e6-c533-4e09-aa5a-e11d53f2ee8a","year":2023},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.624754Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:8656428992598ab3aa8d4b06c6c886ea5d68085125d1650eee33f61aa2517ba3","observation_id":"2971671f-ea87-4faf-80be-1c49b010e8c0","resolution":{"observed_at":"2026-08-12T14:05:13.090339Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15005","last_updated":"2023-05-24T10:45:25Z","snapshot_observed_at":"2026-08-10T05:38:31.450631Z","submitted_at":"2023-05-24T10:45:25Z","title":"Sentiment Analysis in the Era of Large Language Models: A Reality Check","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15005","snapshot_observed_at":"2026-08-12T14:05:11.633024Z","title":"Zhang, Y","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.633024Z"},"links":{"cited_paper":"/paper/2305.15005","citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:e8039a6d7027d43746a6c9c95ca6e82b75dba2e5a515b743c43b65cd875d954e","observation_id":"20f74f79-3b46-4872-be53-a68af548c25e","resolution":{"observed_at":"2026-08-12T14:05:11.633024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.053543Z","title":null,"venue":null,"work_id":"78cec9e9-ab17-4e71-af96-604870bfaa5d","year":1996},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.638406Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:a4e07030c4196fb57ba91d3d61a09b430f81ac3dbdf18fd7a12498e0ff4ea2de","observation_id":"42305955-ce5e-46ca-943d-0339c920341a","resolution":{"observed_at":"2026-08-12T14:05:13.060084Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1057/jors.1993.6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:11.764861Z","title":null,"venue":null,"work_id":"2d3ac3dd-369d-46fe-ac39-a63bfaf08569","year":1993},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.646345Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:3e8480955d04abd65d077aaf0ee8aed06b6031f6c506177f6762c5d528ff714d","observation_id":"f54b60ca-549f-4fc8-b3ef-26cb721f7e8c","resolution":{"observed_at":"2026-08-12T14:05:11.772767Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:05:13.025418Z","title":null,"venue":null,"work_id":"dc52eacb-b6af-4784-9262-35d868fa25a9","year":1992},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.655439Z"},"links":{"citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:0b1d66997db904499b9938a24862e57767c50e34e0a28d59d820613e09aba1f4","observation_id":"57db06dd-88e2-44ec-9952-9a738609ccb9","resolution":{"observed_at":"2026-08-12T14:05:13.033567Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03932","last_updated":"2020-05-11T20:49:28Z","snapshot_observed_at":"2026-08-03T04:44:07.942145Z","submitted_at":"2019-02-11T15:03:30Z","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.03932","snapshot_observed_at":"2026-08-12T14:05:11.666683Z","title":"Zhang, C","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.666683Z"},"links":{"cited_paper":"/paper/1902.03932","citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:1fa2c44375d399af719e9ead760f160c99438bf694d86e3781eda1a201b1013a","observation_id":"2ee18eac-1c88-435c-b3a7-29e49af9ce8a","resolution":{"observed_at":"2026-08-12T14:05:11.666683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.6676","last_updated":"2013-01-23T15:56:44Z","snapshot_observed_at":"2026-08-12T18:25:35.875902Z","submitted_at":"2013-01-23T15:56:44Z","title":"Inferring Parameters and Structure of Latent Variable Models by Variational Bayes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.6676","snapshot_observed_at":"2026-08-12T14:05:11.672665Z","title":"Attias, Inferring parameters and structure of latent variable models by variational bayes, arXiv preprint arXiv:1301.6676 (Jan 2013)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-12T14:05:11.672665Z"},"links":{"cited_paper":"/paper/1301.6676","citing_paper":"/paper/2411.15674"},"observation_digest":"sha256:dd3f29aa046fc5166cd9e7b7f7d74745b35dfd6ec3ed8ded5e79eb3e3e2f3a80","observation_id":"3239a348-3034-47eb-b02a-2f42cb75423c","resolution":{"observed_at":"2026-08-12T14:05:11.672665Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.15674","last_updated":"2024-11-24T00:00:10Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T13:59:40.840868Z","submitted_at":"2024-11-24T00:00:10Z","title":"Quantile deep learning models for multi-step ahead time series prediction"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":5,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":69,"verified_exact":7,"verified_fuzzy":18},"total_outbound_references":102},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 100 of 102 outbound references and 1 inbound Pith citation observation for arXiv:2411.15674."}