{"as_of":"2026-08-21T07:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2de6b142071b32156f1792831fd6dd596377e2221c4e9c137b40735f1353e1ae","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":41,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:03:42.092231Z","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":"2026-07-08T04:14:29.621429Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2310.10688","last_updated":"2024-04-17T18:24:45Z","snapshot_observed_at":"2026-08-16T13:07:39.813820Z","submitted_at":"2023-10-14T17:01:37Z","title":"A decoder-only foundation model for time-series forecasting","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-16T18:07:21.246053Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2310.10688"},"observation_digest":"sha256:8f050b440e07d7b6b9be092855be977479b872700a8df5c7486a57c3a981b226","observation_id":"83f93c78-f4a8-4498-9560-fe111d4a31c4","resolution":{"observed_at":"2026-05-16T18:07:21.296466Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2410.23222","last_updated":"2026-05-28T08:52:37Z","snapshot_observed_at":"2026-08-16T13:04:27.029903Z","submitted_at":"2024-10-30T17:12:03Z","title":"Dataset-Driven Channel Masks in Transformers for Multivariate Time Series","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-23T18:38:24.883332Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2410.23222"},"observation_digest":"sha256:36adb9c4b69daabfdd8d10afa71b818d22f0da296b333b44808520132ac6d09a","observation_id":"fb4ab67d-cd11-487b-9ad1-8d83cc01b74b","resolution":{"observed_at":"2026-05-23T18:43:19.309977Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-12T18:41:37.016115Z","title":"Monash time series forecasting archive","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.11340","last_updated":"2024-11-18T07:15:23Z","snapshot_observed_at":"2026-08-18T19:19:26.967708Z","submitted_at":"2024-11-18T07:15:23Z","title":"A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-12T18:41:37.016115Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2411.11340"},"observation_digest":"sha256:7b884049e3eb5ab652620d4b1fa28c0573e751c63a1b17270b3bdf74dac8d773","observation_id":"bf2f869b-9813-4119-887e-bdda84de4b21","resolution":{"observed_at":"2026-08-12T18:41:37.016115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-11T16:43:21.410669Z","title":"Monash time series forecasting archive,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09880","last_updated":"2024-12-13T05:51:00Z","snapshot_observed_at":"2026-08-17T16:15:57.071445Z","submitted_at":"2024-12-13T05:51:00Z","title":"Financial Fine-tuning a Large Time Series Model","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T16:43:21.410669Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2412.09880"},"observation_digest":"sha256:148d41825a2d023662f3630fa7984003687a8f306f2611da6315b66a7edc1a67","observation_id":"f1a6d49d-dbeb-47a8-ad68-e236b2bd4656","resolution":{"observed_at":"2026-08-11T16:43:21.410669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-11T11:44:04.788094Z","title":"I.; Hyndman, R","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15315","last_updated":"2024-12-19T17:21:34Z","snapshot_observed_at":"2026-08-16T02:05:22.439244Z","submitted_at":"2024-12-19T17:21:34Z","title":"Enhancing Masked Time-Series Modeling via Dropping Patches","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T11:44:04.788094Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2412.15315"},"observation_digest":"sha256:f04aedee1c31389c3c8473bef44658ab292b7d9b479ce10fe2c885175aaff3a5","observation_id":"f1ea63c1-981e-4449-8c88-69e1175cb44f","resolution":{"observed_at":"2026-08-11T11:44:04.788094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-10T23:21:23.642691Z","title":"Monash time series forecasting archive","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.20810","last_updated":"2024-12-30T09:06:47Z","snapshot_observed_at":"2026-08-18T19:25:34.038179Z","submitted_at":"2024-12-30T09:06:47Z","title":"TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T23:21:23.642691Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2412.20810"},"observation_digest":"sha256:1b679f756a91b55670f0c3a06ba7661a5b5a6796cba5787ea12f3ddf08ab1f7b","observation_id":"fb6b0e68-aae9-4d80-9a68-a987ea42e82f","resolution":{"observed_at":"2026-08-10T23:21:23.642691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-10T16:35:14.390716Z","title":"I., Hyndman, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13041","last_updated":"2025-05-20T07:40:13Z","snapshot_observed_at":"2026-08-16T02:04:38.459254Z","submitted_at":"2025-01-22T17:40:17Z","title":"TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T16:35:14.390716Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2501.13041"},"observation_digest":"sha256:1e551d37529227d43aaa382dd4109659c51b1cf11ffaf951aeae62ba7e6df3d6","observation_id":"f5bab3df-a967-46e5-bb0d-050a5cf417f6","resolution":{"observed_at":"2026-08-10T16:35:14.390716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-10T13:55:05.216051Z","title":"Monash time series forecasting archive","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.15942","last_updated":"2025-01-27T10:40:38Z","snapshot_observed_at":"2026-08-15T01:44:37.925561Z","submitted_at":"2025-01-27T10:40:38Z","title":"TimeHF: Billion-Scale Time Series Models Guided by Human Feedback","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T13:55:05.216051Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2501.15942"},"observation_digest":"sha256:48158604090cf667e8e7325746d3e01b1deb187d9b9cb6be80d1ec5f4a7cf043","observation_id":"ee0cb296-26a2-43eb-ac50-d2ec3f7753b3","resolution":{"observed_at":"2026-08-10T13:55:05.216051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-09T05:01:32.354984Z","title":"Monash time series forecasting archive","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.03383","last_updated":"2025-02-05T17:18:55Z","snapshot_observed_at":"2026-08-16T02:04:39.624397Z","submitted_at":"2025-02-05T17:18:55Z","title":"Transformers and Their Roles as Time Series Foundation Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T05:01:32.354984Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2502.03383"},"observation_digest":"sha256:713a75a9f71ad0f0d435cd7569cdf749208d3b895de2ceb6bbd38744f2e02573","observation_id":"5096176a-01ba-48ae-87f2-bf46c5710ed7","resolution":{"observed_at":"2026-08-09T05:01:32.354984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-15T21:03:42.092231Z","title":"Monash time series forecasting archive.arXiv preprint arXiv:2105.06643, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.11163","last_updated":"2025-05-16T12:07:17Z","snapshot_observed_at":"2026-08-17T13:49:29.172564Z","submitted_at":"2025-05-16T12:07:17Z","title":"Foundation Time-Series AI Model for Realized Volatility Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:03:42.092231Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2505.11163"},"observation_digest":"sha256:35865bcd36c430218d721ec7ba3f0b12cb72cb84fd360ae347fae9662edb23ba","observation_id":"5f86bb7d-4ddb-454a-a029-38349050f0c6","resolution":{"observed_at":"2026-08-15T21:03:42.092231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-15T20:57:50.306937Z","title":"Monash time series forecasting archive","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.11390","last_updated":"2025-05-16T15:55:34Z","snapshot_observed_at":"2026-08-18T21:26:43.816259Z","submitted_at":"2025-05-16T15:55:34Z","title":"IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:57:50.306937Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2505.11390"},"observation_digest":"sha256:337d1376f5a18003a2e60a12b816a463781792c402056b3dbee467201ffa5e04","observation_id":"3a5f7adc-4819-4a3c-ba68-a09ec5b9c1f9","resolution":{"observed_at":"2026-08-15T20:57:50.306937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-15T20:39:01.651475Z","title":"I., Hyndman, R","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.12544","last_updated":"2025-05-18T21:01:45Z","snapshot_observed_at":"2026-08-16T07:53:39.635211Z","submitted_at":"2025-05-18T21:01:45Z","title":"Alternators With Noise Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:01.651475Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2505.12544"},"observation_digest":"sha256:3cc1e31db02b8819f0c72d744d8f2dad87d171c7f8982daa03c358a1565f708b","observation_id":"0c79ca91-2719-4aaf-b1e5-82cffedfaf42","resolution":{"observed_at":"2026-08-15T20:39:01.651475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-07T15:27:19.898165Z","title":"Webb, Rob J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15072","last_updated":"2025-05-30T01:59:05Z","snapshot_observed_at":"2026-08-20T17:53:33.062744Z","submitted_at":"2025-05-21T03:39:42Z","title":"MoTime: A Dataset Suite for Multimodal Time Series Forecasting","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:27:19.898165Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2505.15072"},"observation_digest":"sha256:3a28f234ff6518ccd27a7749d90be28444d8895ab9a7154db20993a0e54b066a","observation_id":"5cc004af-feb1-4749-a442-ba529c2d961d","resolution":{"observed_at":"2026-08-07T15:27:19.898165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-07T14:45:24.820327Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.17871","last_updated":"2025-05-27T03:26:51Z","snapshot_observed_at":"2026-08-20T01:41:50.473388Z","submitted_at":"2025-05-23T13:20:47Z","title":"BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:45:24.820327Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2505.17871"},"observation_digest":"sha256:c9928ca356786606b6126281943f7f1e85b092ddc4c082ac6cfca7c704460c67","observation_id":"e0a11785-835e-4b8e-86c5-1a9c556a581f","resolution":{"observed_at":"2026-08-07T14:45:24.820327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-07T13:02:34.612790Z","title":"I., Hyndman, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23017","last_updated":"2025-07-26T14:24:12Z","snapshot_observed_at":"2026-08-13T23:13:55.886096Z","submitted_at":"2025-05-29T02:52:59Z","title":"$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting","version":3},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T13:02:34.612790Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2505.23017"},"observation_digest":"sha256:ba85a047dcb4cea53c28f714c3fa47f7fff3c516034ec5a46aee2c07bfad0372","observation_id":"69cc9911-1cde-4c96-874f-59a6546b6c19","resolution":{"observed_at":"2026-08-07T13:02:34.612790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-07T12:07:02.677364Z","title":"arXiv preprint arXiv:2105.06643","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00457","last_updated":"2025-05-31T08:24:01Z","snapshot_observed_at":"2026-08-16T02:04:36.032132Z","submitted_at":"2025-05-31T08:24:01Z","title":"Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:02.677364Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2506.00457"},"observation_digest":"sha256:4a40679928caaec6983f525f19814fc5e58a81776ddc69a34c5734f90ad5f68c","observation_id":"bca924a3-2f2a-450c-b904-daf557a82817","resolution":{"observed_at":"2026-08-07T12:07:02.677364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-07T06:08:56.121503Z","title":"I., Hyndman, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.06005","last_updated":"2025-06-06T11:49:37Z","snapshot_observed_at":"2026-08-15T03:02:00.840853Z","submitted_at":"2025-06-06T11:49:37Z","title":"LightGTS: A Lightweight General Time Series Forecasting Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T06:08:56.121503Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2506.06005"},"observation_digest":"sha256:8fe4b9bb1bf28a3bf865070df799f1f0c8d6d214e47a6a03e0bdbbb8b5c1ba18","observation_id":"415598c2-a630-4763-a838-f4a9641eaf36","resolution":{"observed_at":"2026-08-07T06:08:56.121503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-05T15:45:36.234748Z","title":"Webb, Rob J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19609","last_updated":"2025-08-27T06:44:46Z","snapshot_observed_at":"2026-08-20T18:48:17.654770Z","submitted_at":"2025-08-27T06:44:46Z","title":"FinCast: A Foundation Model for Financial Time-Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T15:45:36.234748Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2508.19609"},"observation_digest":"sha256:e4a7214ac3a8ec5b29bb4e47c3ae3663a07a2835b5f1affbe079da2a5eb8f451","observation_id":"bdd02486-bdeb-4331-af24-3427fc710ea0","resolution":{"observed_at":"2026-08-05T15:45:36.234748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-05T15:10:28.336847Z","title":"I.; Hyndman, R","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.20437","last_updated":"2025-08-28T05:27:45Z","snapshot_observed_at":"2026-08-14T09:35:08.471176Z","submitted_at":"2025-08-28T05:27:45Z","title":"On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-05T15:10:28.336847Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2508.20437"},"observation_digest":"sha256:3b5b990a57ee0ab452a0417d8d391e7ad7e38053833b9e0170d79f7cd5eab561","observation_id":"749defa8-d920-4b06-b943-53bbc87f95dc","resolution":{"observed_at":"2026-08-05T15:10:28.336847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2509.14933","last_updated":"2026-05-12T02:57:02Z","snapshot_observed_at":"2026-08-02T04:45:44.136735Z","submitted_at":"2025-09-18T13:14:10Z","title":"DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-18T15:30:20.060374Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2509.14933"},"observation_digest":"sha256:a2b010439651541fb4cffbc0ad06095f0a47c04e519b75060f3d776dd43f5261","observation_id":"fcf2ab35-3135-450a-b1c6-908616ba5093","resolution":{"observed_at":"2026-05-18T15:31:33.423075Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2604.17295","last_updated":"2026-04-19T07:25:45Z","snapshot_observed_at":"2026-08-13T11:17:40.019130Z","submitted_at":"2026-04-19T07:25:45Z","title":"LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T06:22:14.332932Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2604.17295"},"observation_digest":"sha256:97dd970fd30ba0022e219caa51871b99b02dc2bd36bdf696a97962911756071a","observation_id":"7d486b75-5901-41be-bfd9-6456f3dc35a8","resolution":{"observed_at":"2026-05-10T06:26:27.645748Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2605.14343","last_updated":"2026-05-14T04:07:05Z","snapshot_observed_at":"2026-08-20T21:39:00.739049Z","submitted_at":"2026-05-14T04:07:05Z","title":"Nearest-Neighbor Radii under Dependent Sampling","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-15T01:40:22.110189Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2605.14343"},"observation_digest":"sha256:f5733e796d662c623544f727d898324cfbdb9f76dc39165818923d42c98021df","observation_id":"d64f4dbf-45ac-46ae-bb9a-d4feb46ac866","resolution":{"observed_at":"2026-05-15T01:43:27.711807Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2605.15694","last_updated":"2026-05-18T07:43:05Z","snapshot_observed_at":"2026-08-14T22:03:20.491208Z","submitted_at":"2026-05-15T07:33:53Z","title":"Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-20T20:16:32.186504Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2605.15694"},"observation_digest":"sha256:7ff2c002097d9c260c2007c224613574231e69fea88b35acf51a171b05fffedb","observation_id":"0d52dfc7-a640-4561-8672-12956e5ec4e4","resolution":{"observed_at":"2026-05-20T20:18:59.764635Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2605.17340","last_updated":"2026-05-19T02:26:50Z","snapshot_observed_at":"2026-08-15T23:55:27.820389Z","submitted_at":"2026-05-17T09:19:22Z","title":"Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-20T14:42:04.841976Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2605.17340"},"observation_digest":"sha256:e590d3a6e9963a19ff9867f561d88d67835bf8b60fca58ac69c8fa9bfba19f36","observation_id":"315a4308-06b3-4789-bb90-8ab8ec9656b5","resolution":{"observed_at":"2026-05-20T14:43:22.355895Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2605.19462","last_updated":"2026-05-19T07:13:20Z","snapshot_observed_at":"2026-08-15T04:27:37.188478Z","submitted_at":"2026-05-19T07:13:20Z","title":"Quantifying the Pre-training Dividend: Generative versus Latent Self-Supervised Learning for Time Series Foundation Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-20T07:11:21.567203Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2605.19462"},"observation_digest":"sha256:21e67ed4b2e2ebcb457046e9d73d2f8f06801022b1457b2c9e21a11b7d8fdea7","observation_id":"9d223f9c-a065-48dc-b9e2-b86fafb5bcf5","resolution":{"observed_at":"2026-05-20T07:13:06.525341Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2605.24703","last_updated":"2026-05-23T19:01:05Z","snapshot_observed_at":"2026-07-06T23:34:47.734159Z","submitted_at":"2026-05-23T19:01:05Z","title":"TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-30T13:11:48.095713Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2605.24703"},"observation_digest":"sha256:1bcd4ebeecd5199e5327fe6073abb8a4c4ee8c8367c66c4632d08ffa8693ba14","observation_id":"4d0920f5-3632-47d7-a4d8-98a4f6d21cff","resolution":{"observed_at":"2026-06-30T13:14:40.704363Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2605.25166","last_updated":"2026-05-24T16:52:48Z","snapshot_observed_at":"2026-08-13T10:40:09.536170Z","submitted_at":"2026-05-24T16:52:48Z","title":"AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-30T12:34:19.755096Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2605.25166"},"observation_digest":"sha256:d0dee24807a4ef703be75e2c5f631c28c23a0ddef5a79d0362a288d2fc1e58ae","observation_id":"82928799-0201-4292-9917-1317a1488869","resolution":{"observed_at":"2026-06-30T12:34:38.519290Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2605.30002","last_updated":"2026-05-28T14:32:48Z","snapshot_observed_at":"2026-08-17T06:09:55.163510Z","submitted_at":"2026-05-28T14:32:48Z","title":"KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T07:20:58.414177Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2605.30002"},"observation_digest":"sha256:c92ee6f79353d979dc084c4db7053f3ed596562bcf6620044877ebc49c64fd8a","observation_id":"914d2b8b-0a16-45f9-bd97-a64cbda0146b","resolution":{"observed_at":"2026-06-29T07:23:12.663091Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2606.01289","last_updated":"2026-05-31T15:20:14Z","snapshot_observed_at":"2026-08-17T08:44:22.383690Z","submitted_at":"2026-05-31T15:20:14Z","title":"Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T17:20:32.648181Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2606.01289"},"observation_digest":"sha256:4f50e17da2eeee88289df206284ba7e054b4c498e579117de150f27505376ea8","observation_id":"9a9a4493-1bb4-493d-962e-b7a83e127e02","resolution":{"observed_at":"2026-06-28T17:22:24.567520Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2606.03184","last_updated":"2026-06-02T05:41:50Z","snapshot_observed_at":"2026-08-15T18:49:06.181941Z","submitted_at":"2026-06-02T05:41:50Z","title":"FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-28T07:40:28.778752Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2606.03184"},"observation_digest":"sha256:4e2a823ce19eb63a035140abfbefbe1929b254c7aa52e7fb620dfb0e9bfa3a20","observation_id":"9cd054f3-bc23-4e22-bb67-94da75602730","resolution":{"observed_at":"2026-07-02T06:06:41.331288Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2606.05404","last_updated":"2026-06-03T20:20:34Z","snapshot_observed_at":"2026-08-12T20:22:36.691156Z","submitted_at":"2026-06-03T20:20:34Z","title":"Harnessing Generalist Agents for Contextualized Time Series","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T05:45:48.655352Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2606.05404"},"observation_digest":"sha256:eef71d2efb1493380086504c31c6e221983d2fb2b4ed34e5df781e233e391d1e","observation_id":"245cf93e-abdc-4c82-9f56-1e27b9a21c09","resolution":{"observed_at":"2026-07-02T08:46:49.110736Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2606.13338","last_updated":"2026-06-11T13:27:43Z","snapshot_observed_at":"2026-08-09T08:02:19.700751Z","submitted_at":"2026-06-11T13:27:43Z","title":"Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarios","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-27T07:16:26.052149Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2606.13338"},"observation_digest":"sha256:4258895d2be7db3dc92c152377ba4aa2fcc0a4cc76c3d57a1bb84083bfd0fe93","observation_id":"5dc8741b-6193-4faf-993d-8a54a10f12b0","resolution":{"observed_at":"2026-07-03T13:58:22.320695Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2606.24715","last_updated":"2026-06-24T07:19:17Z","snapshot_observed_at":"2026-08-15T19:38:58.704546Z","submitted_at":"2026-06-23T15:36:28Z","title":"Model selection with proper scoring rules on data sets of time series: prefer the mean scaled score","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-06-25T21:44:51.504165Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2606.24715"},"observation_digest":"sha256:3ae173f730dd708325155139c86d6a77a74e58ec653b1cc73adedeb1ade84106","observation_id":"41c77f10-0537-4d99-b5f4-5a4da604fe47","resolution":{"observed_at":"2026-07-04T19:10:05.009228Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2606.24996","last_updated":"2026-06-23T15:59:17Z","snapshot_observed_at":"2026-08-15T11:00:15.260563Z","submitted_at":"2026-06-23T15:59:17Z","title":"From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T00:18:20.146234Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2606.24996"},"observation_digest":"sha256:fad87ab76736f41ebed0dd3b50f8826f332ed38dccefa23baad3b056278490cc","observation_id":"2e0cee63-31cd-4b3f-814b-c1cefe5c2c51","resolution":{"observed_at":"2026-07-04T16:39:58.490324Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2606.26774","last_updated":"2026-07-30T12:56:35Z","snapshot_observed_at":"2026-08-07T17:43:26.211486Z","submitted_at":"2026-06-25T09:02:23Z","title":"End-to-end probabilistic hierarchical forecasting of large hierarchies via probabilistic top-down","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-06-26T03:25:07.440442Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2606.26774"},"observation_digest":"sha256:a8b2aeca920cd34124bfd0218233ca91f82643fb46f599bdcfd65deb8d9407bd","observation_id":"853eee4c-1794-4c65-ac1c-db362f4e3f51","resolution":{"observed_at":"2026-07-04T14:39:57.082283Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2607.01918","last_updated":"2026-07-02T09:16:51Z","snapshot_observed_at":"2026-07-07T00:07:23.664493Z","submitted_at":"2026-07-02T09:16:51Z","title":"Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-07-03T17:34:37.552706Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2607.01918"},"observation_digest":"sha256:89f84c02bf9c915a9e19d143543f8c0667e527f1be47d30b4a751931948f0bd0","observation_id":"225077f2-e2fc-4568-a518-e0abeb4f6b6d","resolution":{"observed_at":"2026-07-03T17:38:43.366033Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":"2105.06643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-08T04:14:29.621429Z","title":"Monash time series forecasting archive","venue":"cs.LG","work_id":"d95cde79-a4c5-456d-be01-f0a990e948da","year":2021},"citing_paper":{"arxiv_id":"2607.06504","last_updated":"2026-07-07T17:04:44Z","snapshot_observed_at":"2026-08-14T22:26:08.761831Z","submitted_at":"2026-07-07T17:04:44Z","title":"RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-08T04:07:59.537908Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2607.06504"},"observation_digest":"sha256:1336ff60b92953b0ce07af1e7b07b991396c06821af9a8f159b03e7f0f62bc09","observation_id":"6eb679db-e9d7-43dd-a161-a377595271cd","resolution":{"observed_at":"2026-07-08T04:14:29.622628Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-07-31T19:49:56.293022Z","title":"arXiv preprint arXiv:2105.06643 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28035","last_updated":"2026-07-30T11:18:18Z","snapshot_observed_at":"2026-08-20T04:56:48.359079Z","submitted_at":"2026-07-30T11:18:18Z","title":"Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework","version":1},"reference_index":187,"source":"arxiv_source","source_observed_at":"2026-07-31T19:49:56.293022Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2607.28035"},"observation_digest":"sha256:0f7cf9aa76c893c0b22820a130136824d780d3db6906d6ffa617315fdaa2bffb","observation_id":"94fdba08-2486-4a78-8577-6b230eb9a137","resolution":{"observed_at":"2026-07-31T19:49:56.293022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-15T14:34:14.480970Z","title":"arXiv preprint arXiv:2105.06643 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06867","last_updated":"2026-08-07T06:46:58Z","snapshot_observed_at":"2026-08-20T04:51:28.575652Z","submitted_at":"2026-08-07T06:46:58Z","title":"LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers","version":1},"reference_index":177,"source":"arxiv_source","source_observed_at":"2026-08-15T14:34:14.480970Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2608.06867"},"observation_digest":"sha256:1a9fecd4fe42b9d5e19228bc5cda749d173c96a71757d9c58381ee91e93cea59","observation_id":"b6e16284-cda3-4fb0-b1e2-5ce616dd8241","resolution":{"observed_at":"2026-08-15T14:34:14.480970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-11T00:31:24.775571Z","title":"arXiv preprint arXiv:2105.06643 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.07681","last_updated":"2026-08-07T18:09:31Z","snapshot_observed_at":"2026-08-15T08:05:20.477436Z","submitted_at":"2026-08-07T18:09:31Z","title":"PhysAttNet: Enhancing Predictive Performance in Industrial and Astrophysical Time Series via Physics-Informed Attention","version":1},"reference_index":120,"source":"arxiv_source","source_observed_at":"2026-08-11T00:31:24.775571Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2608.07681"},"observation_digest":"sha256:6279c24efa04125f76f43228db5488f621e81efcbf7735e6e1cc0fee726781aa","observation_id":"2604d930-4f67-4979-a34b-af4c8457d80e","resolution":{"observed_at":"2026-08-11T00:31:24.775571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-11T12:39:57.059435Z","title":"Monash time series forecasting archive,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-19T04:33:37.919111Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.059435Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:e3efdbae11f46eda291aa116165aad091cbeb03b1a313cb50c650fc8e9a6ba56","observation_id":"8798a95b-5241-4789-9509-06eb97dc16de","resolution":{"observed_at":"2026-08-11T12:39:57.059435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2105.06643/citation-record","integrity":"/paper/2105.06643/integrity","json":"/paper/2105.06643/citation-record.json","paper":"/paper/2105.06643"},"outbound":[],"paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T21:36:00.307198Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 inbound Pith citation observations for arXiv:2105.06643."}