{"as_of":"2026-08-06T17:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a382aaf96129d20b31f97e491f3856dad79d04958e32821940d1a8a029342f80","coverage":[{"denominator":78,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":78,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-17T05:42:45.075521Z","state":"measured"},{"denominator":80,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":80,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T05:22:08.579832Z","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":"2026-05-25T05:25:23.783659Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"cited_work":{"arxiv_id":"2511.18539","doi":null,"metadata_source":"pith","pith_arxiv_id":"2511.18539","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","venue":"cs.LG","work_id":"6d660278-9732-41f4-936e-28a1a8eff99d","year":2025},"citing_paper":{"arxiv_id":"2605.15513","last_updated":"2026-05-15T01:16:12Z","snapshot_observed_at":"2026-08-01T17:14:08.866403Z","submitted_at":"2026-05-15T01:16:12Z","title":"CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-19T15:39:56.255871Z"},"links":{"cited_paper":"/paper/2511.18539","citing_paper":"/paper/2605.15513"},"observation_digest":"sha256:76c8f9f4261eb557950ef997767637fd279425478a18d1a29ca2f0850c9ac9a5","observation_id":"ecdd23a2-9614-4b16-ade9-09a473db7d90","resolution":{"observed_at":"2026-05-19T15:42:38.329816Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"cited_work":{"arxiv_id":"2511.18539","doi":null,"metadata_source":"pith","pith_arxiv_id":"2511.18539","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","venue":"cs.LG","work_id":"6d660278-9732-41f4-936e-28a1a8eff99d","year":2025},"citing_paper":{"arxiv_id":"2605.23074","last_updated":"2026-05-21T22:13:20Z","snapshot_observed_at":"2026-07-06T23:33:19.375527Z","submitted_at":"2026-05-21T22:13:20Z","title":"PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-25T05:22:08.579832Z"},"links":{"cited_paper":"/paper/2511.18539","citing_paper":"/paper/2605.23074"},"observation_digest":"sha256:ef8ffc96e592a03f3e076023f3b6f35a36c73c681aa87162d59895356faca57d","observation_id":"390335d0-6fcf-4120-aee3-fe80029a3847","resolution":{"observed_at":"2026-05-25T05:25:23.786214Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2511.18539/citation-record","integrity":"/paper/2511.18539/integrity","json":"/paper/2511.18539/citation-record.json","paper":"/paper/2511.18539"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner T¨urkmen, and Yuyang Wang","venue":null,"work_id":"54205817-48f2-43b0-bc4e-d067548b0301","year":2019},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:c1f5e7605de03635150cdbbb925c23ade3bfa584497e69a87b80bb7202da0b66","observation_id":"594fc25d-558a-4249-86c9-e739e56efeba","resolution":{"observed_at":"2026-05-17T05:44:08.485919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A convergence analysis of gradient descent for deep linear neural networks","venue":null,"work_id":"b3f4bc37-6591-4190-9275-67c42b757d37","year":2019},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:5a865fec1fe62e16c3f07355567fc21e331eac9340216b5e3a45c242119596ee","observation_id":"2edc71e6-86c2-4c12-a30a-c592ccd1d65c","resolution":{"observed_at":"2026-05-17T05:44:08.496637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"TACTis-2: Bet- ter, faster, simpler attentional copulas for multivariate time series","venue":null,"work_id":"71f8c725-7ab1-4c9d-88aa-221b5357311c","year":2024},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:7722092f5ae8bbddeb0fc4a37d5b21db5e82a4366d7562f90745c66f61a3dfbb","observation_id":"f6e5fee4-bcb8-430e-8049-23deafe08fc6","resolution":{"observed_at":"2026-05-17T05:44:08.481471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2e199481-4867-4179-b9c0-1510bcfef489","year":2016},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:e208c4b56a92ab277e1fa5297b58488960b781771232e882582e7947fb0d54e5","observation_id":"d784276a-191a-42f2-ba2f-e86a9434657a","resolution":{"observed_at":"2026-05-17T05:44:08.488283Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Bengio, P","venue":null,"work_id":"3ded5e71-346d-49e6-a8b7-c40e68d28842","year":1994},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:b682c5dbc3b29fdf7ae4d931a6c0f3841e28aef3d9b69dcf42de220f0ad42d05","observation_id":"67a4e41d-6264-47b4-867a-9755c205afe5","resolution":{"observed_at":"2026-05-17T05:44:08.514204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Bicriteria approxima- tion algorithms for the submodular cover problem.Advances in Neural Information Processing Systems, 36:72705–72716","venue":null,"work_id":"716fe666-f343-4eed-bda4-26fea43118bf","year":null},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:b414776b628f4948304b63ebc42bb4bb4b30dda85357d62e54062c6e1ea08c49","observation_id":"d5bc2fc6-dea2-4149-8cfe-66b903967e8a","resolution":{"observed_at":"2026-05-17T05:44:08.417237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Adap- tive threshold sampling for pure exploration in submodular bandits","venue":null,"work_id":"d688f987-7834-4c7f-b115-846d95489b80","year":null},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:d410162b780ef69c6f2d22d67f6b6860b7ab06ce66d4aae1ff6e32d1413cebca","observation_id":"3fa5a285-d68f-4549-80ad-68daf7942da1","resolution":{"observed_at":"2026-05-17T05:44:08.470058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04804","last_updated":"2024-07-05T18:37:09Z","snapshot_observed_at":"2026-08-06T10:05:35.557030Z","submitted_at":"2024-07-05T18:37:09Z","title":"Fair Submodular Cover","version":1},"cited_work":{"arxiv_id":"2407.04804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.04804","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fair submodular cover","venue":null,"work_id":"07320dcf-71d9-4b87-a919-f718b32b1024","year":2024},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"cited_paper":"/paper/2407.04804","citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:085618ce29500c585f5fe17df680dd4d1973bacdff15aba077494b905fb6a6a3","observation_id":"1d03c959-e028-484e-95b6-6cbf34a143e0","resolution":{"observed_at":"2026-05-17T05:44:07.316096Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Learning phrase representations using RNN encoder–decoder for statistical machine translation","venue":null,"work_id":"6daae9e7-c439-4220-9be6-16ea54945309","year":2014},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:3505cf50f1af62818bb7e1ca9586ac05892fb71223ac4fee78e09e05e6a2d83d","observation_id":"bad3005e-5aec-4d55-9041-0078566a702a","resolution":{"observed_at":"2026-05-17T05:44:08.472121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Winner- takes-all for multivariate probabilistic time series forecast- ing","venue":null,"work_id":"335d21b1-d1c9-4a1a-8153-30feb005bb9b","year":2025},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:fc6856311afa6e345c191ea9ea8a18970783c0463897d44b6c193d2c563ef0ee","observation_id":"93626322-28bb-48cb-85e7-b22c1eee80f2","resolution":{"observed_at":"2026-05-17T05:44:08.591989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Developing a novel recurrent neural network architec- ture with fewer parameters and good learning performance","venue":null,"work_id":"db6c0fce-5a9f-4a48-94b3-f4899be6ca03","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:cced3ce239dd94e407d36cfeaf52f3947ca39eecebf9021bf4b74b1fdb91a0f5","observation_id":"8f5d7fd4-946a-4996-966a-7813dedda64e","resolution":{"observed_at":"2026-05-17T05:44:08.467785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Progress in research on implementing machine conscious- ness.Interdisciplinary Information Sciences, 28(1):95–105","venue":null,"work_id":"36788631-d1da-4832-81f7-437c11ac51f8","year":null},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:f7b2300698e3fa693d77961c5d5678436b2a797afe71351fdac9d2555fde41df","observation_id":"aab809fe-8a92-4c76-9832-a0bb61ca1535","resolution":{"observed_at":"2026-05-17T05:44:08.458485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Long-term forecasting with tiDE: Time-series dense encoder.Transactions on Machine Learning Research","venue":null,"work_id":"8f7fe8e5-571e-4bfa-ab8e-563902bc43f7","year":2023},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:1f5f1c6cda762efc50292bbfba598ceef487aabd5f7816f474f74c657d06643c","observation_id":"58eef411-7fea-4ffc-861f-6a49580f6dc3","resolution":{"observed_at":"2026-05-17T05:44:08.463505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Greedy function approximation: A gradi- ent boosting machine.The Annals of Statistics, 29","venue":null,"work_id":"36a3cec8-e2c8-4df2-b5b7-1ac965d04281","year":2000},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:e792538660065a553de39175f20f5dd8fddfaed694cf68f4ef3218ec1c056170","observation_id":"c0dc5d32-375f-4d98-96a8-bb87dafd5361","resolution":{"observed_at":"2026-05-17T05:44:08.474425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Gray.Vector Quantization and Signal Compression","venue":null,"work_id":"ccf3bd68-bf8e-4aac-964e-fe5ee0c8930d","year":1992},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:78aca5662cebe2ec45e64af999c583cf04e938e34d8537a0339e30ef5c0b8aa7","observation_id":"9e0da5d0-09ac-4699-9af6-bf8fb41b2686","resolution":{"observed_at":"2026-05-17T05:44:08.490505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Strictly proper scor- ing rules, prediction, and estimation.Journal of the Ameri- can Statistical Association, 102(477):359–378","venue":null,"work_id":"ad2b9e80-b237-427d-9930-f339c145c72b","year":2007},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:bff6dd837a47d3eee7002ae62d825f7f816df7b34d1c4437dc1ed45edd04a227","observation_id":"d1ce0111-6708-4d23-9e0d-fc7ce8a9556c","resolution":{"observed_at":"2026-05-17T05:44:08.550923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Multiple choice learning: Learning to produce multiple structured outputs","venue":null,"work_id":"f0c9d5dd-da32-44a4-a003-09f1a0049244","year":2012},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:4b2df34d212e759287c77d682f55b7dad9673f0865f8c4e484d28a9fd3cffc05","observation_id":"e509212b-6ff0-4e2e-88b5-e12fbdb0e96b","resolution":{"observed_at":"2026-05-17T05:44:08.456133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Recurrent neural networks for time series forecasting: Current status and future directions.International Journal of Forecasting, 37(1):388–427","venue":null,"work_id":"ebaba966-1205-4231-9568-cf37e5f92274","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:339c4bd006335a32a98917d686bfbde3c771de4b3550e62f839067e5ce9a4420","observation_id":"78073bc1-a4cd-4f0d-bb5e-f60a2cf2e208","resolution":{"observed_at":"2026-05-17T05:44:08.461181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Ho and M","venue":null,"work_id":"de1665c7-1aed-4622-a097-1fb1d4b2e0e8","year":1998},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:60591aecc829b996655f6807be28618caafbafea57e6e1df660bc0d59305811f","observation_id":"8047cd95-b893-4aa2-aad0-790514e9f60d","resolution":{"observed_at":"2026-05-17T05:44:08.465515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Long short-term memory.Neural Computation, 9(8):1735–1780","venue":null,"work_id":"33b6dd83-1e71-48e0-8239-3d23561ce027","year":1997},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:5f061c889cc43350cf6ffbbe6bf498263bceaf613d95a88dc5135853b78d6adb","observation_id":"14d82179-2fae-4511-88a9-f745d0678902","resolution":{"observed_at":"2026-05-17T05:44:08.476485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Li, Sheng Wang, Jiheng Zhang, Ziyun Li, and Tianlong Chen","venue":null,"work_id":"6c78f0a0-1efb-4cd3-9efc-e97d69b9a019","year":2025},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:8f3f445322a2064b8c41a22d05b61ed61eabb40c0c73f021bf870868e18631bb","observation_id":"102a50f7-979f-404f-a610-c06935c15130","resolution":{"observed_at":"2026-05-17T05:44:08.493702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Decorre- lated batch normalization.2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 791–800","venue":null,"work_id":"03f27886-1a3b-4aac-8b18-0644fb9f59f0","year":2018},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:8d6153c1333b384085cc900c3c64e1c3c1b8abb24aa4cfb7bcd01d7f5a78d002","observation_id":"ed6a9946-8bc4-4766-9ebf-d50044ad5498","resolution":{"observed_at":"2026-05-17T05:44:08.438388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"OTexts, Australia, 2nd edi- tion","venue":null,"work_id":"723ee0aa-b954-478c-bc91-2959c387d3b2","year":2018},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:ae99508c07150f7da6d007f57c45ffc8ac96d4b094b711ea18895e37474971b8","observation_id":"873db495-c3b5-43f0-acfe-4e6faee7b280","resolution":{"observed_at":"2026-05-17T05:44:08.440847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A state space framework for automatic fore- casting using exponential smoothing methods.International Journal of Forecasting, 18(3):439–454","venue":null,"work_id":"f32fd380-4bb5-4d0d-8e9a-25c732462f90","year":2002},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:80144342f9f696605e26c1ec33a9a94538a0ac2d1241f56b2af7f613669e3f88","observation_id":"2a6be2c8-202b-4bb3-8308-b6e9cf016667","resolution":{"observed_at":"2026-05-17T05:44:08.446404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Batch normalization: Accelerating deep network training by reducing internal co- variate shift","venue":null,"work_id":"c4d2e3f3-d61a-4529-ad4e-fa0952e52e83","year":2015},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:8aa749ef502b4190d769d8ec6b80ff91925cf180c487bf5922639d695cf83766","observation_id":"c043416f-2ba1-448f-b105-281e313e30f7","resolution":{"observed_at":"2026-05-17T05:44:08.430538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.01575","last_updated":"2026-04-22T04:43:19Z","snapshot_observed_at":"2026-08-03T00:15:02.300580Z","submitted_at":"2025-08-03T04:03:13Z","title":"KANMixer: a minimal KAN-centered mixer for long-term time series forecasting","version":2},"cited_work":{"arxiv_id":"2508.01575","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.01575","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"KANMixer: a minimal KAN-centered mixer for long-term time series forecasting","venue":"cs.LG","work_id":"8b75712d-5246-4bc1-9ec8-91c8a866cad7","year":2025},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"cited_paper":"/paper/2508.01575","citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:3be5b7732c304ae58fdd3e1aff23f662adcffc605789dcda01ff151d31b324a3","observation_id":"c2fb1473-28ad-4593-82ac-b70515dec182","resolution":{"observed_at":"2026-05-17T05:44:07.325254Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A style-based generator architecture for generative adversarial networks","venue":null,"work_id":"34b2bed7-f692-4ae7-99d2-b1f99da2857d","year":null},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:69d959e882500d7c034252ed784e612e2efe003403efdef6889e5f540d9a2083","observation_id":"7f489c46-cc6c-4a49-b39c-c7e86956f49d","resolution":{"observed_at":"2026-05-17T05:44:08.422258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A comprehensive survey of deep learning for time series forecasting: Architectural diversity and open challenges","venue":null,"work_id":"ac2b49d2-be57-4f5e-88cc-15b9987bd230","year":2025},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:4c596e3f7df3adb297a8d81538e685510ecf4d7956a1f5b9cdab1fc2dff0ca32","observation_id":"8817db7f-bced-4065-906b-f407e7c01b9c","resolution":{"observed_at":"2026-05-17T05:44:08.517492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Similarity of neural network representa- tions revisited","venue":null,"work_id":"5cb4e331-bc3f-4650-8b5e-43313d664585","year":2019},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:d7c95f591082d90108b3483d9ca0d346839921002cada2cc1036feb9db49607a","observation_id":"30ae4cb9-80e5-4ad8-a20e-8aecb694ecdc","resolution":{"observed_at":"2026-05-17T05:44:08.505473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Modeling long- and short-term temporal patterns with deep neural networks.The 41st International ACM SIGIR Conference on Research & Development in Information Re- trieval","venue":null,"work_id":"54ded16b-a4c9-4eb4-b0fd-9da0ad3748ca","year":2017},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:809e1a5b603a9be3afe9cd398ec88c8a4d5ffd52e1d980405eb5bdc3037b7b6d","observation_id":"d1e55b32-5a88-4b3a-a81a-e72e1372cb71","resolution":{"observed_at":"2026-05-17T05:44:08.433235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Simple and scalable predictive uncertainty esti- mation using deep ensembles","venue":null,"work_id":"a8cc2642-8c9e-4dbd-b9a6-e73ee4b02ca0","year":2017},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:b7d7fdfea346b2bb9a54697b6a05593e7ecef8ce369018636be3ebcaaaccb7d5","observation_id":"dfc05f7f-2c3a-4cd6-ba26-7e7c1dc0a9dd","resolution":{"observed_at":"2026-05-17T05:44:08.479369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Deep learning.Nature, 521:436–44","venue":null,"work_id":"78118e97-a06f-4f2e-9b00-ba724d152a04","year":2015},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:d32ff92273fb7a54917c5b6bc1e9a3638aac9c5824212c65ef174edc6c1fec8d","observation_id":"3d3c4ded-d245-41d0-b0c6-f9719fa9fbaf","resolution":{"observed_at":"2026-05-17T05:44:08.508069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"LeCun, L ´eon Bottou, Genevieve B","venue":null,"work_id":"b66e27b9-58bc-4278-9dcd-545a13fed1de","year":2012},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:f9115a14261f88684eeb047970e9c560dfde2a35c26726614949d99a6b686bf3","observation_id":"adbb302d-71b5-4d06-9a39-60aaea18fc81","resolution":{"observed_at":"2026-05-17T05:44:08.583928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"11c6b94e-f710-4c81-a81f-514833029fe6","year":2016},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:c036e1a768b9e5618a8258214575caf48ba070c1b456d6b77d672c250daeea1f","observation_id":"9f4dc028-7c11-4e1b-b3eb-00078d57d6f0","resolution":{"observed_at":"2026-05-17T05:44:08.581422Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Winner-takes-all learners are geometry-aware conditional density estimators","venue":null,"work_id":"04d12436-36d9-4292-ac03-47940664e07d","year":2024},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:b0154eefc70196ab93e76e9de471d1a23e1e32ebd5816e009f7112bfd69fdfd2","observation_id":"22086385-8409-4142-ae74-14e9e740c16f","resolution":{"observed_at":"2026-05-17T05:44:08.574142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","venue":null,"work_id":"9d191c28-213e-4d48-8385-92c2e11cd9fa","year":2018},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:6a5e4104f0d906781bd47f6f76c8766fbdceab4c55b340c2e0643b4956f8ac4d","observation_id":"42179249-9465-4b82-80c7-a3db4623d354","resolution":{"observed_at":"2026-05-17T05:44:08.576471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"RMLP: A reparameterized MLP-like network for long-term time se- ries forecasting","venue":null,"work_id":"b3f6b046-1303-49aa-a56c-adce44abc318","year":2024},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:a2230de88ddca4d19f756acc91d33a5f330e293d290fd24d21f17e43a5f899ec","observation_id":"8acbffe6-1182-4c5f-9647-3f9b144b47ab","resolution":{"observed_at":"2026-05-17T05:44:08.579230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Time series forecasting with deep learning: a survey.Philosophical Transactions of the Royal Society A, 379(2194):20200209","venue":null,"work_id":"0fc4ad09-0013-4efc-9bfc-6e8d1d7fc6bf","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:a3d08875f49cab960aa92866580e77095845ffb0283bb4ae8fee32d83a1bad65","observation_id":"11101eb8-8f03-4fda-ae08-41f9bf5f52b9","resolution":{"observed_at":"2026-05-17T05:44:08.566039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A functional view of quantization and clustering.ESAIM: Probability and Statistics, 21:93–114","venue":null,"work_id":"ab186433-a427-458c-a2b3-dc40980c5710","year":2017},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:220bc4be8431589cf029e38c0ffebe33cb546b9db48d93b0e97387e26453730a","observation_id":"f761a49b-a854-49e8-bdb6-7528ec66fb80","resolution":{"observed_at":"2026-05-17T05:44:08.563146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Treernn: Topology- preserving deep graph embedding and learning","venue":null,"work_id":"82cfe0dd-cfb4-40a5-ac95-be2756cd78e7","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:9961d6a1d62b4b9285a48eb73fae79be2d465112d11fc9c0f555a8e2d9a33179","observation_id":"b3159c65-7ef2-4fc8-a84b-0298f8496c8a","resolution":{"observed_at":"2026-05-17T05:44:08.568808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Web traffic time series forecasting.https : / / kaggle","venue":null,"work_id":"c01af481-85a4-4eac-86fe-aa0d5d359636","year":2017},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:892b5bb014c080594a72705c18326390116aa0926d3031c161369de606d285ce","observation_id":"108fe59e-645e-40c4-8c7a-0613ca2f7dd9","resolution":{"observed_at":"2026-05-17T05:44:08.586407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Implicit regularization in deep learning: A view from function space.Advances in Neural Information Processing Systems (NeurIPS), 30","venue":null,"work_id":"e2765efa-b9b9-4ac8-9bff-f4caf50b8804","year":2017},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:58a7710f7b01ac29508564470cb2efb301df216979135193c1c8c295b07d0216","observation_id":"1cf74bd9-bfb4-43df-b8bb-6a4afee1ab75","resolution":{"observed_at":"2026-05-17T05:44:08.571393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":"6cceaa53-9162-41d6-a8cc-ac1a5c2d1f53","year":2023},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:6018aebbde2e17704472f9ddf07974eefb4c3770285706b009b882bbe9bbce65","observation_id":"a281fe44-cfff-4403-85e3-1096c2e461e6","resolution":{"observed_at":"2026-05-17T05:44:08.560342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Multiple choice learning for ef- ficient speech separation with many speakers","venue":null,"work_id":"d2329d2b-8eef-42a1-ba77-ed65850eab0d","year":2024},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:5d6a679416c72a1cafbccfaa877ac05ff6357f2aa1290743aa086901399e812b","observation_id":"a2cc00a0-a547-4c34-94d7-8eca20019f44","resolution":{"observed_at":"2026-05-17T05:44:08.553460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"An- nealed multiple choice learning: Overcoming limitations of winner-takes-all with annealing","venue":null,"work_id":"530ed293-316a-4108-ac58-ec2ac0b71f35","year":null},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:99071c23d8f0686efc5e5704be36456ab470df9e409ac2c3367077ae34b7d31f","observation_id":"d13f73de-4736-4adb-8413-e889d0c1a476","resolution":{"observed_at":"2026-05-17T05:44:08.555576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Multi-choice learning for multimodal sequence prediction","venue":null,"work_id":"d8abf734-26aa-4f75-9da6-f0e98cb184f6","year":2024},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:8fe24b67a5e235ee8416763fe15f7188e08d1e109db9e2af7ab86fdab7158d21","observation_id":"fa7ec7a3-f99f-4c1a-9329-0a6e30e02c49","resolution":{"observed_at":"2026-05-17T05:44:08.557785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Lawrence.Dataset Shift in Ma- chine Learning","venue":null,"work_id":"51927b14-51ad-4f4b-9947-a16e5fe59bf7","year":2009},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:22909faf07452ab5be48324bce87fa3db0545f3b8cff502b617639e1494fe4ca","observation_id":"6ece566b-5e25-4916-8c35-8d7144c39f7f","resolution":{"observed_at":"2026-05-17T05:44:08.548706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Rajagukguk, Raden A","venue":null,"work_id":"e3be1ac5-0759-49cc-9d46-91d3cfb6c236","year":2020},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:856ddc2cb72b5e12549b0fbccc1f454c5d1ced362743f1b0a27b68370dd6d123","observation_id":"742a3aad-0ab1-4ccd-8d80-e237b03a7c37","resolution":{"observed_at":"2026-05-17T05:44:08.540929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.06103","last_updated":"2021-01-14T19:15:12Z","snapshot_observed_at":"2026-08-06T10:47:38.624796Z","submitted_at":"2020-02-14T16:16:51Z","title":"Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows","version":3},"cited_work":{"arxiv_id":"2002.06103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.06103","snapshot_observed_at":"2026-07-03T13:58:22.311322Z","title":"Multi-variate probabilis- tic time series forecasting via conditioned normalizing flows","venue":null,"work_id":"1bf2b9ac-52c7-4349-92a6-ce99afd25770","year":2002},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"cited_paper":"/paper/2002.06103","citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:e8ac806e556dee9d8ff527c98780a5669a0f40f607c7e8cad617b0187325165f","observation_id":"73be22e4-61bd-4128-95e3-4140d87f5897","resolution":{"observed_at":"2026-05-17T05:44:07.329742Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.12072","last_updated":"2021-02-02T12:32:30Z","snapshot_observed_at":"2026-07-06T10:36:31.297391Z","submitted_at":"2021-01-28T15:46:10Z","title":"Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting","version":2},"cited_work":{"arxiv_id":"2101.12072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2101.12072","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting.CoRR, abs/2101.12072","venue":null,"work_id":"8f120a0e-67eb-470f-9b03-39b73cf94b79","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"cited_paper":"/paper/2101.12072","citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:c066decf4822a395fd46f33ca570102f5c7dcf3686595900c44d723bed1c95b7","observation_id":"6a79f625-4000-4f32-8f5b-89f2286938a4","resolution":{"observed_at":"2026-05-17T05:44:07.334699Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Structured basis function networks: Loss- centric multi-hypothesis ensembles with controllable diver- sity","venue":null,"work_id":"92dfb790-8959-44c3-8bc8-78439ce115db","year":2025},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:6fe0f7dd87146189d1b755260f61bd31588d817c669eb7216fcf18f9724c3676","observation_id":"6f9e2cb5-c0c3-48d3-b204-40962c6dd241","resolution":{"observed_at":"2026-05-17T05:44:08.589077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Learning representations by back-propagating er- rors.nature, 323(6088):533–536","venue":null,"work_id":"409cc662-596f-4b5a-8610-fa64e3e3916b","year":1986},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:e5d0fc2486ed78a67a450be425928fef766b668fa4b9acc36489d45062714cb5","observation_id":"9e1f4ad0-7ab2-4a9c-8193-8141dbd71563","resolution":{"observed_at":"2026-05-17T05:44:08.539013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Learning in an uncertain world: Representing am- biguity through multiple hypotheses","venue":null,"work_id":"1cf968e1-f446-46da-aac2-1fe29c111aa5","year":2017},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:282731f1ab0e79c12d6d89ade1b2de00f767b57b93ce682ff5d2b41e302ace49","observation_id":"dc612a6b-2495-4281-893c-8f2d0abc6c49","resolution":{"observed_at":"2026-05-17T05:44:08.419712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Deepar: Probabilistic forecasting with autoregressive recurrent net- works.International Journal of Forecasting, 36(3):1181– 1191","venue":null,"work_id":"72d3a950-1eac-41f8-9680-d1dc2d33743d","year":2020},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:6876750cb8d642a64d8fe3653871637589b522c2a950bfb3660c940b19a96146","observation_id":"54309d37-40d2-46ee-b806-4d643442da92","resolution":{"observed_at":"2026-05-17T05:44:08.424946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Trajectory-wise mul- tiple choice learning for dynamics generalization in rein- forcement learning","venue":null,"work_id":"ec347e11-9c5f-4779-acc6-cff379acdb75","year":2020},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:116346f554248bbb3f536f371e8f873d58554e47aff7626bbde522c8196f36d2","observation_id":"3783ff41-6046-4314-bd70-863a0f80525f","resolution":{"observed_at":"2026-05-17T05:44:08.427910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Trajectory-wise multi- ple choice learning for dynamics generalization in reinforce- ment learning","venue":null,"work_id":"e96f0a01-94ec-441a-91c3-f9e258c619db","year":2020},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:00276539d672a3efd884d856998a56757e167c6578a35dcacafd5074da5573cb","observation_id":"5778440c-4b73-4ee1-a8ca-e4f213a96436","resolution":{"observed_at":"2026-05-17T05:44:08.449305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Recursive and di- rect multi-step forecasting: the best of both worlds","venue":null,"work_id":"4d19536a-f751-4c73-b1e8-a0a3587d26f7","year":2012},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:692a31dbd42eaa5c4f6fb346de757b596241419285333c0221bb53ade4dffd31","observation_id":"b4c3f17c-a4d7-42d7-9c5a-6bfcf99f0df9","resolution":{"observed_at":"2026-05-17T05:44:08.414522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.08022","last_updated":"2017-11-06T14:21:43Z","snapshot_observed_at":"2026-08-05T08:35:49.218794Z","submitted_at":"2016-07-27T10:23:00Z","title":"Instance Normalization: The Missing Ingredient for Fast Stylization","version":3},"cited_work":{"arxiv_id":"1607.08022","doi":"10.48550/arxiv.1607.08022","metadata_source":"pith","pith_arxiv_id":"1607.08022","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Instance Normalization: The Missing Ingredient for Fast Stylization","venue":"cs.CV","work_id":"426062a5-47a3-410c-8843-c4e2e3c91ca0","year":2016},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"cited_paper":"/paper/1607.08022","citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:5394055acb4be2896c45fa924496dfc07d8992190e7edfb2236816587a53c679","observation_id":"857bdd29-8186-4268-b179-e24c9a0e8549","resolution":{"observed_at":"2026-05-17T05:44:07.320293Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-25T20:53:20.982129+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T20:53:20.982129+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Ramjattan, and Antonio Carta","venue":null,"work_id":"5cac4ceb-9540-4d67-9826-f6396dcd7c74","year":2024},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:3723b73781a86162bb748185ecbbd84ab21bd695dcd8e13eed2ee508d1dbba20","observation_id":"432ce1bf-e78c-4dbf-8334-539d9aa72f92","resolution":{"observed_at":"2026-05-17T05:44:08.501871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-07-10T15:27:20.392773Z","title":"Attention is all you need","venue":null,"work_id":"f4f211f6-7f43-42a0-ae0b-df15f7ece604","year":null},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:82eda456a0c6148fe85310957e4d4fd871d1176097ffd7537aac86c09e31ad86","observation_id":"9247bc55-8b36-45d6-b8a6-0c933c6c9460","resolution":{"observed_at":"2026-05-17T05:44:08.400236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Zhang, and JUN ZHOU","venue":null,"work_id":"30a59081-4ed1-4a56-b79b-c03714086cab","year":2024},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:4163b3a97bb0261e068c143ecd98a59950f825a8228bdaff71e6977b0b742b9a","observation_id":"d02898b3-492d-4516-acd6-df0bc6dd649c","resolution":{"observed_at":"2026-05-17T05:44:08.402760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Maddix, Jan Gasthaus, Dean Foster, and Tim Januschowski","venue":null,"work_id":"a60fbaeb-a0ed-4a4c-b979-e0be5a323f18","year":2019},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:109de17e183a8f28a18c5408e53c044793a42b7fa862d8231427d4a5c8adb0fc","observation_id":"58675b4a-0931-4ab0-a8f3-8ebd4ab3f3e3","resolution":{"observed_at":"2026-05-17T05:44:08.409118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A multi-horizon quantile recurrent forecaster","venue":null,"work_id":"b3bad106-1118-4a56-8976-8a1e8d531b2b","year":2017},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:409da1dac35bfcb08818b632670a528310b5824a10210a00b4beb8951ec32898","observation_id":"034448b2-dd8f-4bb2-b36a-8ae7bfb4c43a","resolution":{"observed_at":"2026-05-17T05:44:08.411909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Autoformer: Decomposition transformers with auto- correlation for long-term series forecasting","venue":null,"work_id":"cbef1c24-574a-4627-a596-82d98a9ce824","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:fd672191576c9bb2b59f21476419b449949ddc541d882a2309b4f9de2c6dec9a","observation_id":"397014ca-0ec5-437d-bb3a-d34b52904a2e","resolution":{"observed_at":"2026-05-17T05:44:08.453251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Interpretable weather forecasting for worldwide sta- tions with a unified deep model.Nat","venue":null,"work_id":"f6d9fd2b-1e25-4ae7-a679-7080b80bee07","year":2023},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:d1e550a999d0c498ccd0247be58cf461d9985898daee8ce0a551091edb427f60","observation_id":"78abf1a1-195d-4c5a-9e74-d8d2ccc65362","resolution":{"observed_at":"2026-05-17T05:44:08.435762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Group normalization","venue":null,"work_id":"d82c62a3-ad31-47ab-8586-588086bdd846","year":2018},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:fcf8c4b987c4b4643bc8ffe6b012327566a5f90b4491272eed3abef875655418","observation_id":"2a7ead53-50d7-419d-85d2-24883ddc5d30","resolution":{"observed_at":"2026-05-17T05:44:08.499220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Graph wavenet for deep spatial-temporal graph modeling","venue":null,"work_id":"627ca67f-fa1c-4ef3-a8d8-9b1c2c6150e7","year":1907},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:0cd2b6989f32001c1c40e821d05d98a4e054bb7623157e0babcb478bba8f662c","observation_id":"492dd8a3-4c84-4277-9bc8-d5e9984d9cb3","resolution":{"observed_at":"2026-05-17T05:44:08.543340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Relation is an option for processing context information","venue":null,"work_id":"ec52f486-6fd7-4fe8-91b6-db45779db84e","year":2022},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:e2e5d883445724a4957c4e7f8f950f8ef542492f56401c293c8cd9f7fff70d39","observation_id":"cbb55a83-c9ba-4841-9456-1c2e9bd0357f","resolution":{"observed_at":"2026-05-17T05:44:08.525940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Barlow twins: Self-supervised learning via redundancy reduction","venue":null,"work_id":"dfbecd94-c710-434e-9815-7b52f88c0245","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:f3f6dbf7f4b54a65727ba34267148e0c3a97fd5788f5dd5f66f624dfbe390c58","observation_id":"30c1349b-3383-4793-8797-baca6455313c","resolution":{"observed_at":"2026-05-17T05:44:08.528834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Are transformers effective for time series forecasting? InPro- ceedings of the AAAI Conference on Artificial Intelligence, pages 11121–11128","venue":null,"work_id":"3e26778a-57f7-48e2-9c3b-80c4de770fb1","year":2023},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:b03b17d404ae0568a9ad40501d2da5d6b809334ab03aa43b693d23b5fd38b7de","observation_id":"bb7aa787-eb28-4eea-a77c-2c46921f3e37","resolution":{"observed_at":"2026-05-17T05:44:08.533807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Understanding deep learning re- quires rethinking generalization.Communications of the ACM, 64(3):107–115","venue":null,"work_id":"6cd67a0f-f68d-4cbc-8c48-4b357123e1dc","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:6e7051eb9725588a2ca0ba551b989cc742ff0da799f5e74c26e37c34fbee6cb0","observation_id":"b9a97e61-a434-4ee3-8b6a-4b8beef150d7","resolution":{"observed_at":"2026-05-17T05:44:08.531156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"mixup: Beyond empirical risk minimiza- tion","venue":null,"work_id":"64dd558b-9c9a-4ab4-b6ba-f4a8e0dd4cd0","year":2018},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:774f19506cb1611c713391ed5444b2fdd9af34c12da3a781b464ac637b7708ad","observation_id":"8666f9c7-bb15-4fb1-a95f-1582966e0a0b","resolution":{"observed_at":"2026-05-17T05:44:08.545692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Deep spatio- temporal residual networks for citywide crowd flows predic- tion","venue":null,"work_id":"1ec40c2f-d8e4-49d7-bf29-766bf2e24bc0","year":2017},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:39a62afadb4f302b1668c4a8a9e56e2e6cf8137b658619622d66e24dce596928","observation_id":"8337554b-4899-4374-935a-96a1ac9da76a","resolution":{"observed_at":"2026-05-17T05:44:08.511210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Gps: A probabilistic distributional similarity with gumbel priors for set-to-set matching","venue":null,"work_id":"91545d15-fd5c-4a3b-939a-2a5e86ff1bc1","year":2025},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:01b99afa8499b98f79af0440627857752c069aff592a4895a4fcb921c8cc600d","observation_id":"b15714ca-8dca-403a-8ebc-894e7a681eb7","resolution":{"observed_at":"2026-05-17T05:44:08.483841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecast- ing","venue":null,"work_id":"b7363f88-f8e3-43b7-83b3-81ca2046cbd0","year":2021},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:f29f8e47edc4c14ac1e889f4437916fc3cdbee78df2337da99a8423414a7b6ca","observation_id":"361b6226-b842-4e63-8b31-54e2f446b365","resolution":{"observed_at":"2026-05-17T05:44:08.443791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Fedformer: Frequency enhanced decom- posed transformer for long-term series forecasting","venue":null,"work_id":"87cf3e24-ddd7-4999-84d2-56b7d72c6519","year":2022},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:1203410f602d04e848271235f99fa55e2928c890ac82773c88ea22c70988d382","observation_id":"f193ac9c-0fff-4b3f-8b93-ecc60d19fda5","resolution":{"observed_at":"2026-05-17T05:44:08.536352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Multiple Choice Learning (MCL) The Multiple Choice Learning framework provides an ef- fective paradigm for modeling diverse outcomes under un- certainty","venue":null,"work_id":"5d1372d2-6138-4ed9-952a-7024e12dd75a","year":null},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:a29aac53f6f0e57313f79edd92985eb3cc156939cc9b49289ecc68157eeacbcb","observation_id":"b437e53e-f03e-403b-91bd-9ac65d877c9a","resolution":{"observed_at":"2026-05-17T05:44:08.520256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"90bff367-dada-466a-aec9-bc9a5be70662","year":2000},"citing_paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-17T05:42:45.075521Z"},"links":{"citing_paper":"/paper/2511.18539"},"observation_digest":"sha256:a262768705cf7ef1c2197c8ee7f611c7d0eff6e8f6f97a134813ff4c7633c775","observation_id":"6c2b99cd-48ca-404f-9cb8-5c3724a46dcf","resolution":{"observed_at":"2026-05-17T05:44:08.523126Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2511.18539","last_updated":"2026-04-23T05:21:00Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-01T01:15:05.827466Z","submitted_at":"2025-11-23T17:10:07Z","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting"},"reference_resolution":{"displayed":78,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":5,"verified_fuzzy":70},"total_outbound_references":78},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 2 inbound Pith citation observations for arXiv:2511.18539."}