{"as_of":"2026-08-11T12:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3ae14048928ad4823c551759ec3bc926644a015ba4d66ff7dead999e9d1a9b33","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T13:36:16.246171Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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":"2026-06-28T15:52:40.568646Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T21:56:16.548859Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"cited_work":{"arxiv_id":"2501.16325","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.16325","snapshot_observed_at":"2026-08-05T00:40:39.026315Z","title":"A., Ott, E., Pomerance, A., Hunt, B., and Girvan, M","venue":null,"work_id":"9f12b547-c22d-4d66-b95e-17be66a1952d","year":2025},"citing_paper":{"arxiv_id":"2509.15105","last_updated":"2026-05-22T12:07:12Z","snapshot_observed_at":"2026-08-04T04:40:44.681486Z","submitted_at":"2025-09-18T16:11:31Z","title":"Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-25T08:22:24.238459Z"},"links":{"cited_paper":"/paper/2501.16325","citing_paper":"/paper/2509.15105"},"observation_digest":"sha256:7a1ffcce47d243da727c270754d821f389c861511597cc650e042b7bd70d9387","observation_id":"adf94433-81c2-408f-b613-7170cb674906","resolution":{"observed_at":"2026-08-05T00:40:39.026315Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"cited_work":{"arxiv_id":"2501.16325","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.16325","snapshot_observed_at":"2026-08-05T00:40:39.026315Z","title":"A., Ott, E., Pomerance, A., Hunt, B., and Girvan, M","venue":null,"work_id":"9f12b547-c22d-4d66-b95e-17be66a1952d","year":2025},"citing_paper":{"arxiv_id":"2606.01999","last_updated":"2026-06-01T09:55:06Z","snapshot_observed_at":"2026-08-02T20:54:13.634326Z","submitted_at":"2026-06-01T09:55:06Z","title":"Why Do Time Series Models Need Long Context Windows?","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-06-28T15:52:40.568646Z"},"links":{"cited_paper":"/paper/2501.16325","citing_paper":"/paper/2606.01999"},"observation_digest":"sha256:29143e93443052864a8ba368bddc50958276bcc6e2e53368aa954592a551096a","observation_id":"f39b3266-318e-4876-82f7-97fb1875b01a","resolution":{"observed_at":"2026-08-05T00:40:39.026315Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.16325/citation-record","integrity":"/paper/2501.16325/integrity","json":"/paper/2501.16325/citation-record.json","paper":"/paper/2501.16325"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.920946Z","title":"Price , author A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.920946Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:3abc092b768904a1210181500296836089050a678c673ba32c6159cf2e1822a8","observation_id":"d8ee2b3e-d448-46f5-949c-09c3b2989439","resolution":{"observed_at":"2026-08-10T13:36:15.920946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.926145Z","title":"Arcomano , author I","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.926145Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:a27ef89fb7c2a1dc9f8b72c7da6ad5e3c26475623b469ec2912da79fc0240656","observation_id":"28fdc33c-d0e4-4dd5-9569-529a6b3a247d","resolution":{"observed_at":"2026-08-10T13:36:15.926145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1162/netn_a_00252","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.822083Z","title":"Wein , author A","venue":null,"work_id":"77f82106-21b3-4cda-bcf9-730dafb6509d","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.930860Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:ec5efe9f21815ab25b2758ee49c9dc39a69b9cf9a69bc50109d0899548472d2e","observation_id":"39c90d7f-69c5-4b3e-bd6c-4d43afdf36bc","resolution":{"observed_at":"2026-08-10T13:36:16.827514Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.935721Z","title":"De Matola \\ and\\ author C","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.935721Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:d572b337bef422bccbe9c85927fb08688960332f2cbd53405b14761d3f37c435","observation_id":"66530d30-17b2-4c26-9c18-8db8be20ca13","resolution":{"observed_at":"2026-08-10T13:36:15.935721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ijforec","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.806811Z","title":null,"venue":null,"work_id":"e94ecb87-56c2-4e79-b858-9439641a4edf","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.940418Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:71c9a2fc74af7aed19e0d628229a9409e0e28c3acac7d87b5a6abd41422c4ac2","observation_id":"84418f1d-efc7-4f6b-ad8e-f260e75950d4","resolution":{"observed_at":"2026-08-10T13:36:16.811430Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.945840Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.945840Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:7249f876d4b173312c0010149ac806ad4e06a23968f1d82bb67b34b5ae1c8d09","observation_id":"4be7a8ca-58de-4851-bce9-1292606892dd","resolution":{"observed_at":"2026-08-10T13:36:15.945840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.950268Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.950268Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:24a1f82f4943efa7e22c5f18e8ad19e29c201136077a96de1c78795ac405afc0","observation_id":"14aa396b-d67f-4295-99d0-07ff3b9fa542","resolution":{"observed_at":"2026-08-10T13:36:15.950268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.954585Z","title":"Han , author J","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.954585Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:7bad528877af0961828c2e48ce75405d271648f27ceeb0586f5f9fdd5a33c0b9","observation_id":"a7716cc3-7753-474f-9af0-44b418ab37bb","resolution":{"observed_at":"2026-08-10T13:36:15.954585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.958881Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.958881Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:8ed79084b7d527877040d87f6c8c786bd0a048c80a1b6307ac28208082cf09db","observation_id":"51324ea4-db25-4407-ab22-ea4a9c461338","resolution":{"observed_at":"2026-08-10T13:36:15.958881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.963821Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.963821Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:f29aba2ac63b7e50ce2a33cf09dc707ce0062e00531d9dde18db0d4fa1ade051","observation_id":"dc27b04f-73d8-4f1f-913e-ccdc5663ba65","resolution":{"observed_at":"2026-08-10T13:36:15.963821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.968431Z","title":"Pathak , author A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.968431Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:ad03ea9812512ab197ed0ea8a59bf4f5fd8441ff85cc50d465547900f752e23b","observation_id":"a866e3f0-6f17-434b-84c6-952f456463c7","resolution":{"observed_at":"2026-08-10T13:36:15.968431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.796004Z","title":"Wang , author D","venue":null,"work_id":"08d301e0-9e0c-4c4d-a0b7-3f63192ad70a","year":2021},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.973039Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:faa21ea23da2e82306a6befdf24e6d9318e04c052fa197aad67004d146f3c67d","observation_id":"b566fca4-c999-4411-a273-8abd91fc6073","resolution":{"observed_at":"2026-08-10T13:36:17.800737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18377","last_updated":"2024-06-07T23:38:25Z","snapshot_observed_at":"2026-07-06T17:36:52.920342Z","submitted_at":"2024-02-28T14:52:58Z","title":"Out-of-Domain Generalization in Dynamical Systems Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18377","snapshot_observed_at":"2026-08-10T13:36:15.977570Z","title":"Göring , author F","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.977570Z"},"links":{"cited_paper":"/paper/2402.18377","citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:a3339a20c5dadb737a93b8a4c4fbba986ca357e11df0094c4a639cc21cfa2595","observation_id":"e5050b7a-d854-4028-a1f5-5866ee7704d8","resolution":{"observed_at":"2026-08-10T13:36:15.977570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.982602Z","title":"Zhang \\ and\\ author Q","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.982602Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:e1cced5bc768ef6b2636e5ffa1a10204724fe21c40ee2a1afeee0632eb78db1e","observation_id":"b89a3d95-a76c-4122-9d0f-279147dfc017","resolution":{"observed_at":"2026-08-10T13:36:15.982602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.781059Z","title":"Yang , author Y","venue":null,"work_id":"51b48381-2450-4a3e-bcaa-9cc9219d5289","year":2020},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.987654Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:7f0e5d6aa9c3f83db12dc66bea8004e767abd9e199e1fd988b8593e2ebe6556c","observation_id":"6031c62d-dab5-480f-82dd-638c69483a17","resolution":{"observed_at":"2026-08-10T13:36:17.785936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:15.992420Z","title":"Hospedales , author A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.992420Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:f702ceea14f40111ab3b3f9fe6e87f46e84620d5871a23c5284e793d0d35c687","observation_id":"2e2c5ad3-890f-4379-8206-7415ac304360","resolution":{"observed_at":"2026-08-10T13:36:15.992420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.765344Z","title":"Brazdil , author J","venue":null,"work_id":"21f719ef-55c0-4faa-abe8-f74ab11f2270","year":2022},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:15.996802Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:3c7cab2a6accef04d372e91563adea2b97c1e56ebce25221105e7c131c9fa6a0","observation_id":"31e77993-09dc-42c1-bc28-d448b0fe31a7","resolution":{"observed_at":"2026-08-10T13:36:17.770110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10462-013-9406-y","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.751156Z","title":"Lemke , author M","venue":null,"work_id":"a10a3c99-b2b4-4047-b4c1-12e44cd47e43","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.001152Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:b2f95e36c7e3bc11d07a3e42e77617dd7d51752688cc041c8e9d5333e3b3f440","observation_id":"a2cf804a-9df9-4c9d-8468-3abaa8859c18","resolution":{"observed_at":"2026-08-10T13:36:16.755669Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.005732Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.005732Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:924173cb54dd63338ac3603766819767e0de59f4b01d26e67b4ceaa6afae80c7","observation_id":"9a1cbb59-7824-4c15-9213-a48f22e8f0cc","resolution":{"observed_at":"2026-08-10T13:36:16.005732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.011573Z","title":"Feurer , author J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.011573Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:33b5751a7f7d020cbc71688f539a08b98e32df8260e31c98d253c2d656035195","observation_id":"b5ca06c4-e9ad-4ed8-aa77-659f65a21512","resolution":{"observed_at":"2026-08-10T13:36:16.011573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neucom.2009.09.020","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.713045Z","title":"Lemke \\ and\\ author B","venue":null,"work_id":"c356832d-d98e-47d3-a2bb-55d7e97f5742","year":2009},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.016538Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:03d9f614ece3659c013e473fc0e545c49eed5c2bb6a9be3b9f597a030c92f86a","observation_id":"86cd37ab-660d-404e-bef1-f113513c6708","resolution":{"observed_at":"2026-08-10T13:36:16.718307Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.022285Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.022285Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:0ee7bb20d8fa77f13e2e27ad542b44280806f4f09f3a44ce95f995592730b2cc","observation_id":"985b0c9a-9e4d-4406-936a-02797e525018","resolution":{"observed_at":"2026-08-10T13:36:16.022285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.750408Z","title":"Finn , author P","venue":null,"work_id":"49f77a5e-0bd3-4dee-b70a-6eff9bc67576","year":2017},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.027183Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:224818ff1387d214d0fa2b13e0bb2f186eeb1d57a5c78a2924a270b82eba3cbe","observation_id":"bc01bacb-8db4-4b56-b6f4-fa89d3e69b80","resolution":{"observed_at":"2026-08-10T13:36:17.755057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.05301","last_updated":"2019-11-15T21:15:22Z","snapshot_observed_at":"2026-07-06T07:52:43.720801Z","submitted_at":"2019-05-13T21:56:37Z","title":"Hierarchically Structured Meta-learning","version":2},"cited_work":{"arxiv_id":"1905.05301","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.05301","snapshot_observed_at":"2026-08-10T13:36:17.145842Z","title":"Hierarchically Structured Meta-learning","venue":"cs.LG","work_id":"e5167b09-87e1-442c-83b9-b57e1fc92158","year":2019},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.031850Z"},"links":{"cited_paper":"/paper/1905.05301","citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:3665936f2f7de9b4d8061df6f15526d18b3aa180cd39e71a7947a04965c3481e","observation_id":"7890cc1b-bd34-43a5-b2c9-218be14aa124","resolution":{"observed_at":"2026-08-10T13:36:17.152141Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.735502Z","title":"Raghu , author M","venue":null,"work_id":"839ddab1-bf35-44d2-96e2-43969bb29761","year":2020},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.036941Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:afe45b57960f783ba04952765e682490c9e8a9b46be96d658e13ccfa072774fb","observation_id":"f9ac6e2c-c1bf-4de5-a044-c1c28968f200","resolution":{"observed_at":"2026-08-10T13:36:17.740239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.720923Z","title":null,"venue":null,"work_id":"6fbb0db2-dbe6-41d2-a1a1-cde95f32c5bd","year":2019},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.041630Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:99f344f724b750d3abf22093e1394866886e2e05ed4469eb6ca85bea20e3abf7","observation_id":"d8c41c04-42ac-4b90-b89e-34a1f74181c1","resolution":{"observed_at":"2026-08-10T13:36:17.725315Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.09912","last_updated":"2018-07-26T01:26:14Z","snapshot_observed_at":"2026-07-06T06:52:21.185384Z","submitted_at":"2018-07-26T01:26:14Z","title":"Meta-learning autoencoders for few-shot prediction","version":1},"cited_work":{"arxiv_id":"1807.09912","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.09912","snapshot_observed_at":"2026-08-10T13:36:17.123134Z","title":"Meta-learning autoencoders for few-shot prediction","venue":"cs.LG","work_id":"c80a9f7e-bbc2-479c-8548-caf98ec23deb","year":2018},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.045580Z"},"links":{"cited_paper":"/paper/1807.09912","citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:25b5ef8c384c35a62302a7b9a7177cd1ee285410084628c362303bd773f739fd","observation_id":"4cfe1c07-7b33-4ec8-8e5f-5ae47daca547","resolution":{"observed_at":"2026-08-10T13:36:17.128795Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2139/ssrn.4393300","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.685162Z","title":"Joshaghani , author S","venue":null,"work_id":"7ffea8a1-91cf-455d-92d3-83b5911cfae9","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.050135Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:7134f73d4bf9b9dc01e0e089462e1dca30e84309fe10b7070fc2160fabbbf478","observation_id":"24ba46d6-3a45-44d4-a43e-8c565e22bc20","resolution":{"observed_at":"2026-08-10T13:36:16.690079Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.03722","last_updated":"2021-10-07T18:23:14Z","snapshot_observed_at":"2026-08-03T02:26:16.512820Z","submitted_at":"2021-10-07T18:23:14Z","title":"A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data","version":1},"cited_work":{"arxiv_id":"2110.03722","doi":"10.48550/arxiv.2110.03722","metadata_source":"pith","pith_arxiv_id":"2110.03722","snapshot_observed_at":"2026-08-10T18:16:17.926450Z","title":"A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data","venue":"cs.LG","work_id":"98080b75-f772-4b57-870f-99c671e40377","year":2021},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.054424Z"},"links":{"cited_paper":"/paper/2110.03722","citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:09c3f5aa358b1fbe81ad3a70e535ed4f931770ac0bd098f14563d2dd949a33a4","observation_id":"a1783dfe-b738-464f-af27-01a5390f0418","resolution":{"observed_at":"2026-08-10T13:36:16.674750Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.705712Z","title":"Kirchmeyer , author Y","venue":null,"work_id":"1546838a-c4dc-42c7-84f4-e104ab476b8b","year":2022},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.058956Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:d420f278582d0433606470e6346eafa496ed16f6517869c02e91ed5f689e9cb0","observation_id":"7fdb6b03-5d62-43fd-bc50-d22048cf7fee","resolution":{"observed_at":"2026-08-10T13:36:17.710602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.690754Z","title":"Yin , author I","venue":null,"work_id":"d0c82f51-9cca-4e3f-be0f-3df64d001562","year":2021},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.062891Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:9e74971454d87fb06e541d578b89730d9670f33ae0215156971245f6e8bb2763","observation_id":"dda5d1a6-dd50-4bb3-bcc8-bbf0ce1dd6b6","resolution":{"observed_at":"2026-08-10T13:36:17.695149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.10271","last_updated":"2022-10-11T19:49:51Z","snapshot_observed_at":"2026-07-06T10:43:05.412194Z","submitted_at":"2021-02-20T06:41:08Z","title":"Meta-Learning Dynamics Forecasting Using Task Inference","version":5},"cited_work":{"arxiv_id":"2102.10271","doi":null,"metadata_source":"pith","pith_arxiv_id":"2102.10271","snapshot_observed_at":"2026-08-10T13:36:17.100342Z","title":"Meta-Learning Dynamics Forecasting Using Task Inference","venue":"cs.LG","work_id":"ca1e1c4b-cb83-4209-94a3-1aa176e79de5","year":2021},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.066913Z"},"links":{"cited_paper":"/paper/2102.10271","citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:04602c65d1a6e40e95ee7a8408484c3bd15b20c30157635be78c0131f85ddb23","observation_id":"451fce9b-e304-4f72-a63c-36a169226432","resolution":{"observed_at":"2026-08-10T13:36:17.105849Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01579","last_updated":"2025-01-02T23:57:23Z","snapshot_observed_at":"2026-08-10T22:23:15.864325Z","submitted_at":"2025-01-02T23:57:23Z","title":"Unsupervised learning for anticipating critical transitions","version":1},"cited_work":{"arxiv_id":"2501.01579","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.01579","snapshot_observed_at":"2026-08-10T13:36:17.077664Z","title":"Unsupervised learning for anticipating critical transitions","venue":"nlin.CD","work_id":"4eb5d07f-4083-4238-9203-6a777f462ce1","year":2025},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.071180Z"},"links":{"cited_paper":"/paper/2501.01579","citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:ded46ca5423cfedbb0115091e054fd2b44a7008f4e7fa2504405a181aa53e1b8","observation_id":"f06f90f1-868e-405c-b84b-79ab5f0f7bb5","resolution":{"observed_at":"2026-08-10T13:36:17.082417Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.075609Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.075609Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:98122f83700dc2528d36fd103db9471fdbd0ad6e96f692e8825029e3302e1fe1","observation_id":"d57936c6-1c57-49be-b30e-cf40cc95dfc0","resolution":{"observed_at":"2026-08-10T13:36:16.075609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.079660Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.079660Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:661482e3620e843967b47a3aa4998395c8ef6d83f50f49af7c0a9452d4fd69b2","observation_id":"0399f569-8874-4c21-9ee6-54e4587a6367","resolution":{"observed_at":"2026-08-10T13:36:16.079660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physrevresearch.5.033213","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.625276Z","title":"Zhang \\ and\\ author S","venue":null,"work_id":"697f3cdd-46d7-4b4b-a420-6850569ebca3","year":2023},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.083968Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:66a01331d334a6a75d9597f51e102da4e4840fcb423a9042c2eba9f1faf40260","observation_id":"f329c4d2-ad7d-4017-872c-ea8eaf2006c0","resolution":{"observed_at":"2026-08-10T13:36:16.630118Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.088422Z","title":"Lu , author B","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.088422Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:5dfbf3326631ee484d0e51f09dc18f0ba1dbf785c841a6a5aa59ef96906403fb","observation_id":"5a2b6c13-726f-49fd-9a40-d759a65ad3a5","resolution":{"observed_at":"2026-08-10T13:36:16.088422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.10325","last_updated":"2024-03-15T14:13:49Z","snapshot_observed_at":"2026-07-06T17:45:15.311629Z","submitted_at":"2024-03-15T14:13:49Z","title":"Data-driven cold starting of good reservoirs","version":1},"cited_work":{"arxiv_id":"2403.10325","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.10325","snapshot_observed_at":"2026-08-10T13:36:17.053059Z","title":"Data-driven cold starting of good reservoirs","venue":"math.DS","work_id":"8c02bac5-0ff3-4f9c-b88c-f12cd8777537","year":2024},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.092676Z"},"links":{"cited_paper":"/paper/2403.10325","citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:99c6f0a95da4cff2b0d7320373d153ad72c5e1384bf6607c823728ad958628e4","observation_id":"f4551fb7-982c-4662-9074-b5794f338811","resolution":{"observed_at":"2026-08-10T13:36:17.058056Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1063/5.0055371","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.597248Z","title":null,"venue":null,"work_id":"8a971151-7b77-46c8-8428-31aac0a180ab","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.097001Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:51f4402f8d4f7719c634ef7491598f9f6e2b7bb9774d712c8e5414b494b5bc82","observation_id":"c2f3f19e-cec2-4291-98a2-a36781a66882","resolution":{"observed_at":"2026-08-10T13:36:16.601889Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1063/5.0042598","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.580417Z","title":"Patel , author D","venue":null,"work_id":"6054dec4-735c-4823-925a-e3df71051cc1","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.101195Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:85f912226557c6038dfce6092bedf78013710d652a5f6031f0dfe5ea205f6423","observation_id":"dcfe0ce7-eaec-405c-8305-29831a5e07c5","resolution":{"observed_at":"2026-08-10T13:36:16.585488Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.105538Z","title":"\\ Kong , author H.-W","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.105538Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:03a800e6872a693b00529c7c4ddf72373265d4336d40fb20c84d885f03d94766","observation_id":"fc5884a9-edb2-463a-9a1d-df90b04196bd","resolution":{"observed_at":"2026-08-10T13:36:16.105538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.109836Z","title":"o glmayr \\ and\\ author C. R \\","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.109836Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:13ae55d878bdb3ecfeb2662507f9d5a63e03bb7285aa79d2dfb2e537e9891c41","observation_id":"4350b84e-3023-4a60-b34f-5b25435f3deb","resolution":{"observed_at":"2026-08-10T13:36:16.109836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.114040Z","title":"Panahi \\ and\\ author Y.-C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.114040Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:04e1b70bd1f1cddd7fdfdc6e4db86bb0cd8726325870f525673b5dbe4fca5915","observation_id":"c86750b4-b82b-4cf3-9a2f-25c638fe4a24","resolution":{"observed_at":"2026-08-10T13:36:16.114040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.118583Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.118583Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:450b280285da962a3328fd65962a076ba788355ebcfc6a20bebb281965974d4e","observation_id":"db947d00-e1ab-44b1-82ea-fd2d2081251e","resolution":{"observed_at":"2026-08-10T13:36:16.118583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41467-024-49190-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.523381Z","title":"\\ Kong , author G","venue":null,"work_id":"f294305e-ac88-4979-bd3e-9ac9e5e6569e","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.122849Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:d69f1f28d612edc78237e0c17c8f2e97e1cc461ab5dc9b0cbebe9796373171e6","observation_id":"875da0f5-c695-42b1-a8aa-7f75ab16a375","resolution":{"observed_at":"2026-08-10T13:36:16.528453Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1063/5.0004344","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.508503Z","title":"Lu \\ and\\ author D","venue":null,"work_id":"5955e22c-9536-4510-a7a3-4bff0a013f39","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.127132Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:003ab247512c1009487df01670a2bc002edab0f66484d492e4b1643787d8ad68","observation_id":"cce18e75-bb3d-4c72-96e8-f35affbfdfa6","resolution":{"observed_at":"2026-08-10T13:36:16.512951Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.131981Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.131981Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:32032899fb7c128c74345b8b4133b3641f4789c911e1edaa2432a580f4704ef4","observation_id":"45f6a14e-16e1-485d-90b5-20852d8d6655","resolution":{"observed_at":"2026-08-10T13:36:16.131981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.675678Z","title":"Schrauwen , author D","venue":null,"work_id":"2cc661d7-c910-47c6-b9a6-9908c3b9e099","year":2007},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.136869Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:23099f4f362c3e68dbe1bfbf039b9886ad47f4aa14bc01902262cda92efaaeb6","observation_id":"d7e1774d-bf96-4371-bedf-d78518dd7403","resolution":{"observed_at":"2026-08-10T13:36:17.680414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.32257","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.026548Z","title":"Sun , author M","venue":null,"work_id":"f7dbd8c6-d9c4-4a5d-b117-897cd65ccb78","year":2022},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.141447Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:1a0d206b52b51e809b231b4e415381663b5d2cd86b9bd02beaaaedb8eb83a607","observation_id":"87c9855d-c814-42f9-938f-22ab988d9883","resolution":{"observed_at":"2026-08-10T13:36:17.034603Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.146008Z","title":"Lukoševičius \\ and\\ author H","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.146008Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:8adf1940405a8929587266c4d4b52dbd12cdca59705106533a5027e5672bec9d","observation_id":"9e6206b4-90fe-4d40-8d76-c739d66191e1","resolution":{"observed_at":"2026-08-10T13:36:16.146008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physrevlett.128.164101","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.472188Z","title":"Srinivasan , author N","venue":null,"work_id":"2973a666-7b53-4091-802f-42c875b29402","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.150570Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:484c829a649ac2df612c640e6a9873f4080541c015ed443c5b05d1833a0a80e4","observation_id":"04068ad9-5325-403f-98e6-e0c4fb376855","resolution":{"observed_at":"2026-08-10T13:36:16.476937Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.156193Z","title":"Tanaka et al","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.156193Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:3cac20c550cdde34c7e7b72e3e5c7b02aab4836fe71b138ff91d18429b13b886","observation_id":"a50dd97e-3daf-4075-872c-7dc134b8c0cc","resolution":{"observed_at":"2026-08-10T13:36:16.156193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.160282Z","title":"Lu , author J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.160282Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:c70c6239f5c7e874d153d60807f4594f9955c5a33c76fa0e6dd3b49e6b7599f5","observation_id":"24fc71ff-8490-4c37-996e-bca4ea48f2bf","resolution":{"observed_at":"2026-08-10T13:36:16.160282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.164530Z","title":"Krishnagopal , author M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.164530Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:19460b0aeb1f909ab93a90c18dd5aaae2192de8cdd4c8295864316559a4a36fe","observation_id":"30708115-30c3-47e2-8df3-cbbe7d45f284","resolution":{"observed_at":"2026-08-10T13:36:16.164530Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.168657Z","title":"Pathak , author B","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.168657Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:8aee3540959a111801e6ec145a99c2f56830eb7374dd20afa80ee2f9f039c46f","observation_id":"9bba1e27-be56-4193-9edb-a215792500d6","resolution":{"observed_at":"2026-08-10T13:36:16.168657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.172505Z","title":"Wikner , author J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.172505Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:bb930a4d92efbebfc430ba46006c80f03572ea5cab064ab5ce9abea113f05aab","observation_id":"10e18146-8a67-476a-89e3-4e358d40e9fa","resolution":{"observed_at":"2026-08-10T13:36:16.172505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.176708Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.176708Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:d071a3193ded79e22ab31359c801490eb4b6557a2a6de8adb507fd7e139a988b","observation_id":"addda523-83cc-41a4-afae-2f5b60f44da8","resolution":{"observed_at":"2026-08-10T13:36:16.176708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.180762Z","title":"Yan , author C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.180762Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:f753c9bf69ab7e96d9d1e8c5f86d9a0178632ad87b8cd178305a91b36308e967","observation_id":"faaf3a36-7610-4298-9283-deba973ffda3","resolution":{"observed_at":"2026-08-10T13:36:16.180762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neunet.2023.10.054","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.384604Z","title":"Wikner , author J","venue":null,"work_id":"911c64b2-9d30-475f-9df0-d97b2d04f00e","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.184885Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:90f1772d8e5fa1a8a3e9654cc762d039dfbd13f56d5d424563e0b496ecad9136","observation_id":"eba188d5-d477-45db-94dd-d189d01123dc","resolution":{"observed_at":"2026-08-10T13:36:16.388807Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3402/tellusa.v16i1.8893","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.369800Z","title":null,"venue":null,"work_id":"77976fff-633e-4578-b1d4-90235aa6ade2","year":1964},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.189407Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:071f51b754f60f9dc1fc6dca41f8737b50a2b5714c4b492723ba3011c53bee18","observation_id":"eafeb1c1-be6b-4e33-81c2-316b9931ad08","resolution":{"observed_at":"2026-08-10T13:36:16.374193Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.193840Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.193840Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:d0a568d543c2aab31036454b69964b22bf23001a93b86452c24c222a92f899ae","observation_id":"462f2e47-d433-4b88-8275-636d6ded23a3","resolution":{"observed_at":"2026-08-10T13:36:16.193840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.198085Z","title":null,"venue":null,"work_id":null,"year":1963},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.198085Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:d2de71ee1a0f2b8a6e6fd616a16e981db3c30a53316e62f20e8f76a136d09dc7","observation_id":"e657edc4-8c74-44d7-82bd-af18de41506d","resolution":{"observed_at":"2026-08-10T13:36:16.198085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.659950Z","title":"Tél \\ and\\ author M","venue":null,"work_id":"cd998b9e-6dfd-4a4f-9a2f-77a73f2146dd","year":2006},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.202239Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:a5993979c0e7aac7a83544a95c1f396a0d5edc758d9d040a58b2a0309c6629c3","observation_id":"6191c007-1b70-4fa8-965e-6d0fad5d86d8","resolution":{"observed_at":"2026-08-10T13:36:17.664259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.645565Z","title":"Krishnagopal , author Y","venue":null,"work_id":"379dd547-3c87-49c3-ba23-ffdedc5cb9e5","year":2018},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.206748Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:6ce8afb2409fc3fc140f8f60c937ce23e80f2f7ffb05bbba6c85b4e579a12463","observation_id":"1ff5c762-5e24-46f7-bb98-0d42b332c30d","resolution":{"observed_at":"2026-08-10T13:36:17.649913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.630334Z","title":"Schaetti , author M","venue":null,"work_id":"5532d313-c6c1-4a47-be08-f4b85851c33c","year":2016},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.210981Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:599bf3705320638ddbfc9a7ba0396297e199913bcb3a626c61a646459e39484a","observation_id":"93b5d1dc-3db5-4915-ba79-252b4e3fcabe","resolution":{"observed_at":"2026-08-10T13:36:17.634872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.215286Z","title":"Jaeger \\ and\\ author H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.215286Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:bdeab3edc50bea8d4ba236235a39aa1f4ae0d9387b1a9f1f67833a09cf8356f0","observation_id":"36689652-4762-4d02-9afe-b1833c69e85e","resolution":{"observed_at":"2026-08-10T13:36:16.215286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.613896Z","title":"Canaday , author D","venue":null,"work_id":"c2de5af1-3cd6-460f-91d5-c4c9a1f5f436","year":2024},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.219451Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:cb07b3b2dfcf695808a953fa3236953e5cc12ec85ce2cb9046608136be45c766","observation_id":"f12599da-6c7e-4e08-bafa-aedae26f5ef4","resolution":{"observed_at":"2026-08-10T13:36:17.619234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-94-015-8480-7_2","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.334271Z","title":null,"venue":null,"work_id":"00ce3717-9dc2-444e-97aa-5b3c673cec69","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.223938Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:7f5c0b66b297fbe32bb71deff0b8f98bdf13ffea52e10a1ccf6cbc6ef070a6f5","observation_id":"252d7854-e0a6-48f9-9358-d36304c3c8c5","resolution":{"observed_at":"2026-08-10T13:36:16.339031Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:17.598235Z","title":"echo state","venue":null,"work_id":"71c3f509-c5c9-432e-a180-3ab1c47b9b24","year":2001},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.228335Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:1c2a7537e4fe55f39848d4037817a51b4a5f94796a3203d9bd106998034495c2","observation_id":"4ddfd07a-1833-40cb-ada8-226c2ce8a653","resolution":{"observed_at":"2026-08-10T13:36:17.603222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.232496Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.232496Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:c7f9091487fe8c5227d2d253517c666ee3e98ec65a0a36bc64943036d0ce2055","observation_id":"251b8e4a-b1e9-43b8-bd23-c599e2f1dc14","resolution":{"observed_at":"2026-08-10T13:36:16.232496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.236856Z","title":"Cucchi , author S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.236856Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:f1aa675d168c1e0e47bda3def220d8b14a79aab7d19e587cc45b23d7a19965bc","observation_id":"59cfea50-39a1-4242-bc55-ebcaf043b008","resolution":{"observed_at":"2026-08-10T13:36:16.236856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1063/5.0066013","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.297550Z","title":null,"venue":null,"work_id":"5450ade0-b602-4b5b-aa9e-2b8ff0251b9f","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.241474Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:e0dc082dcee71a4699e22ec6d20cb72454e5eb04e67f594d244500f7e5ca44ce","observation_id":"124fc798-9da9-4de3-b3dd-921314ded781","resolution":{"observed_at":"2026-08-10T13:36:16.302345Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neunet.2022.06.025","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:36:16.280303Z","title":null,"venue":null,"work_id":"e6a8ba22-19e6-4215-83da-450a8f80eb51","year":null},"citing_paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning","version":2},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-10T13:36:16.246171Z"},"links":{"citing_paper":"/paper/2501.16325"},"observation_digest":"sha256:b631943672dc4ecb19e672301f9ef90688db28c332e7deb6f22be4945674f130","observation_id":"a4206f91-2699-4ecd-9667-5ccf5045ac86","resolution":{"observed_at":"2026-08-10T13:36:16.286354Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.16325","last_updated":"2025-07-31T06:57:11Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T02:47:56.892726Z","submitted_at":"2025-01-27T18:58:04Z","title":"Tailored Forecasting from Short Time Series via Meta-learning"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":1,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":37,"verified_exact":19,"verified_fuzzy":13},"total_outbound_references":73},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 2 inbound Pith citation observations for arXiv:2501.16325."}