{"as_of":"2026-08-11T16:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bb839b5b54a903801ccaa3b2f761e4c7c1b9131686ea5cded5e4e8ced643b924","coverage":[{"denominator":67,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":67,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:38:42.329042Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-09T14:46:03.487899Z","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":"2026-05-11T16:51:07.611614Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"cited_work":{"arxiv_id":"2501.01480","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.01480","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Coral: Concept drift representation learning for co-evolving time-series","venue":null,"work_id":"79434a10-8c80-4a04-877b-572d89952fd1","year":2025},"citing_paper":{"arxiv_id":"2605.01295","last_updated":"2026-05-02T07:11:38Z","snapshot_observed_at":"2026-08-10T23:53:35.478090Z","submitted_at":"2026-05-02T07:11:38Z","title":"Autonomous Drift Learning in Data Streams: A Unified Perspective","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-05-09T14:46:03.487899Z"},"links":{"cited_paper":"/paper/2501.01480","citing_paper":"/paper/2605.01295"},"observation_digest":"sha256:5dd66b73f744351d6f3179da3144e08bfd743df0ebe539bfa2a286f950837518","observation_id":"dd5cf2c1-3af2-4ced-a1a8-cc5bd9ef49ee","resolution":{"observed_at":"2026-05-11T16:51:07.616544Z","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/2501.01480/citation-record","integrity":"/paper/2501.01480/integrity","json":"/paper/2501.01480/citation-record.json","paper":"/paper/2501.01480"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:38:44.661022Z","title":"Automatic subspace clustering of high dimensional data","venue":null,"work_id":"cd8c8e9f-1f72-4de7-90d7-4a358127ad6c","year":2005},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:40.865496Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:326590af736dd937daf44fc32e6c6685dffdf563c52f1893db1e5bf56ccf88d2","observation_id":"b717d3a9-709d-40f9-904b-58e8dae61308","resolution":{"observed_at":"2026-08-10T22:38:44.664761Z","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":"2026-08-10T22:38:44.649409Z","title":"Markov-switching garch models in r: The msgarch package","venue":null,"work_id":"ff22ab15-1cb6-4566-997a-0f96b1b7b150","year":2019},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.002783Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:db0812f9020b520e1b0150a92c9de038c28711db074ea9b5dc8657d851db23c7","observation_id":"08186106-0a04-47a5-abf3-73e60fa4c518","resolution":{"observed_at":"2026-08-10T22:38:44.653799Z","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":"2026-08-10T22:38:44.633806Z","title":"and Liang, J","venue":null,"work_id":"3187dbe4-b146-4205-94ae-4fe0acec1d69","year":2020},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.075856Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:f8a1f23781498dc6b1b99953bab4bae53796af5b651e886adad7f5b1908e5f06","observation_id":"ca92f435-6f14-4bae-b89f-09fa25127d10","resolution":{"observed_at":"2026-08-10T22:38:44.639034Z","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":"2026-08-10T22:38:44.570125Z","title":"S., and Kassler, A","venue":null,"work_id":"31490180-01fb-4ff4-870d-633716ae4f07","year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.080309Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:d313e0665010bd5d456526a11c5289088436e03794fa62b7d56c1e65d45e5590","observation_id":"09468136-feaa-4c9b-9838-03b4e2067f09","resolution":{"observed_at":"2026-08-10T22:38:44.596075Z","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":"2026-08-10T22:38:44.557567Z","title":"J., and Lucas, A","venue":null,"work_id":"f5b49f81-fd3a-4c9a-822e-f717f747e057","year":2017},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.085805Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:7c3cc65ff77d1a4872b323eddc248bf0926cd9829275811d2ee0fa423783212b","observation_id":"a596c557-3dad-40bd-9b62-76d2c18df920","resolution":{"observed_at":"2026-08-10T22:38:44.561885Z","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":"2026-08-10T22:38:44.545513Z","title":"and Wang, S","venue":null,"work_id":"54123d55-abab-4328-b185-d32733f744aa","year":2008},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.089996Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:8c2b4c1fe07163fe4d5444ea5db585117d1c65b4b2eec08a066038daff88ce55","observation_id":"098b34db-dd26-4a67-9acd-92a49b4be400","resolution":{"observed_at":"2026-08-10T22:38:44.550066Z","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":"2026-08-10T22:38:44.528931Z","title":"Box and jenkins: time series analysis, forecasting and control","venue":null,"work_id":"204d04c6-cfac-4d27-93ac-e44b29f923f3","year":2013},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.094007Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:09637416f8ee066cd467ef0cfdb544a89d1780cf3f38dfbb6f0682dedfb64903","observation_id":"ec4cbfa8-01d3-4700-beef-ecb56eaa4aa1","resolution":{"observed_at":"2026-08-10T22:38:44.535825Z","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":"2026-08-10T22:38:44.461821Z","title":"C., Minku, L","venue":null,"work_id":"95420b84-4d59-4775-9bda-92af7953fbcd","year":2016},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.098158Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:a8256cff4e79e461eec2093212db41205f9db76bf61b9b84f5466db500ad6e55","observation_id":"d0692118-5388-4780-81d2-5a16d5c50e0b","resolution":{"observed_at":"2026-08-10T22:38:44.478050Z","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":"2026-08-10T22:38:44.449363Z","title":"Self-expressive kernel subspace clustering algorithm for categorical data with embedded feature selection","venue":null,"work_id":"9e0663e4-4797-4c40-a2f4-93c874ef9354","year":2021},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.102035Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:d9c9db15cb0990b8705b294a6184a29248543aaf1d7f18afd943888b16ebfaa2","observation_id":"c56ecbad-97a6-47f6-8e5d-6d49b843b02d","resolution":{"observed_at":"2026-08-10T22:38:44.453829Z","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":"2026-08-10T22:38:44.436045Z","title":"and Paschalidis, I","venue":null,"work_id":"2fc747ee-4598-42f7-bf77-3a99b6a86c33","year":2019},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.105907Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:d4280903a112c1df4472e26e63942735c38ef8aeb592cb0f156d40ef8b18e329","observation_id":"6b22217c-ba25-4ecf-b1ed-88dbffac0269","resolution":{"observed_at":"2026-08-10T22:38:44.440434Z","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":"2026-08-10T22:38:44.385364Z","title":"Clustering-based cross-sectional regime identification for financial market forecasting","venue":null,"work_id":"f91376e3-6c18-4a18-af4c-1a866dea1a2b","year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.109849Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:48602ed27d858fababb68bd0a9a61e6734bee8ea3fd28df2609775672025bce4","observation_id":"6a536420-b92c-4add-b03e-2760bc404266","resolution":{"observed_at":"2026-08-10T22:38:44.426935Z","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":"2026-08-10T22:38:44.329832Z","title":"Dynamic cross-sectional regime identification for financial market prediction","venue":null,"work_id":"fcdf1e56-a9c8-4205-95dc-d3cbd2a786e4","year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.114123Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:5b5f94559c875da59e457688198d0da224aa808c788340c8948f2d5f15f95d2e","observation_id":"5f00d7d5-d3ff-4810-b3b7-822f0ffffce5","resolution":{"observed_at":"2026-08-10T22:38:44.334264Z","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":"2026-08-10T22:38:41.118687Z","title":"Timemae: Self-supervised representations of time series with decoupled masked autoencoders","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.118687Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:0938af30be1f13be4fca12dddd14fd91db8435d2fffd488b0076059f0f86ba2b","observation_id":"850d945d-ee9c-41da-9d19-614681315880","resolution":{"observed_at":"2026-08-10T22:38:41.118687Z","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":"2026-08-10T22:38:44.315834Z","title":"Convex optimization & Euclidean distance geometry","venue":null,"work_id":"242ce876-c144-417d-a785-d5ddd434f18b","year":2010},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.122667Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:e24b5c6037104d3825ee2a5fe1e58531e3896d0820919c08258b1c4f69e8a453","observation_id":"0e19a087-3df0-409c-bef5-a32635ac92e4","resolution":{"observed_at":"2026-08-10T22:38:44.320136Z","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":"2026-08-10T22:38:44.303822Z","title":"V., Xue, H., and Salim, F","venue":null,"work_id":"ebd16be4-3baf-4da2-af25-3925fce22200","year":2021},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.126541Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:b7b36c36cc45b7f91fbf84fdcfb9df2a2f47cfdc5b957793399c192efb91e366","observation_id":"24333f0a-2d9e-44cb-b31a-0e9899a8896a","resolution":{"observed_at":"2026-08-10T22:38:44.307993Z","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":"2026-08-10T22:38:44.207719Z","title":"and Vidal, R","venue":null,"work_id":"be989c2f-c3b9-463b-8838-53f727925844","year":2013},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.130616Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:8a6b42a4b1807143631097a188ab8e068710d3e06b2a5edb7c275797a932e370","observation_id":"b86a5360-eb49-4ad2-82c1-9446580c1f8d","resolution":{"observed_at":"2026-08-10T22:38:44.256565Z","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":"2026-08-10T22:38:44.196609Z","title":"Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting","venue":null,"work_id":"6be2ba15-7a79-4f73-8352-4947f2fbdf9f","year":2023},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.134615Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:6494a1647fc3ddca6f3c61ffef336f0a06c034557738991ce329884b6740158e","observation_id":"583275cd-33f6-484a-8b49-84c3950b5dc5","resolution":{"observed_at":"2026-08-10T22:38:44.200082Z","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":"2310.04486","last_updated":"2024-05-09T10:11:23Z","snapshot_observed_at":"2026-08-09T02:18:07.524681Z","submitted_at":"2023-10-06T15:45:28Z","title":"T-Rep: Representation Learning for Time Series using Time-Embeddings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04486","snapshot_observed_at":"2026-08-10T22:38:41.138671Z","title":"T-rep: Representation learning for time series using time-embeddings","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.138671Z"},"links":{"cited_paper":"/paper/2310.04486","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:7af544fd0812accc4757a133e9c7756ef30bf15d896daabd1e05d02a77b4519f","observation_id":"0c0d4a4a-a125-4018-b1a3-f6338c47f2e9","resolution":{"observed_at":"2026-08-10T22:38:41.138671Z","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":"2026-08-10T22:38:44.182927Z","title":"Efficient dense subspace clustering","venue":null,"work_id":"637e7214-0344-4a4d-bde7-4701f2acec17","year":2014},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.143005Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:dac74afaf0b11fe7aac377369e872fa371a06d7788a0ec238d0bc48210bd2a55","observation_id":"db369fbb-6d5d-43bb-8d6b-e28c1f0dd1a3","resolution":{"observed_at":"2026-08-10T22:38:44.187885Z","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":"2026-08-10T22:38:44.119500Z","title":"Reversible instance normalization for accurate time-series forecasting against distribution shift","venue":null,"work_id":"24d05dbd-89e5-4566-8dc3-5d7498433e66","year":2021},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.147077Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:f1149af94f7ce05c7b4947344f6b2df64a2fd6d308bff8f4eb8be7fdaab8732b","observation_id":"c4d1fb87-a7bd-4e46-89a8-5a44dee442fb","resolution":{"observed_at":"2026-08-10T22:38:44.175023Z","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":"2026-08-10T22:38:44.063867Z","title":"and Hong, Y","venue":null,"work_id":"b1fb09a9-3996-4f56-a439-4c29f6f4d36f","year":2011},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.154906Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:7de2c7b20bac633707efdf0e7000bba0c046d782a7c27edd1417f3c5c7ec1cae","observation_id":"ccb47036-5ea6-4a79-96e3-2010b0b01df0","resolution":{"observed_at":"2026-08-10T22:38:44.067584Z","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":"2026-08-10T22:38:44.051972Z","title":"Ddg-da: Data distribution generation for predictable concept drift adaptation","venue":null,"work_id":"4a6fd424-a692-454c-9c8c-ec1657670ab2","year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.158441Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:e718d2c0a4b0a779d156f0929101a478ee89606ca5e785a303a117b29ca16473","observation_id":"a029be04-d6c3-4749-9302-6c9d2d20fbad","resolution":{"observed_at":"2026-08-10T22:38:44.056010Z","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":"2026-08-10T22:38:44.038037Z","title":"S parse TSF : Modeling long-term time series forecasting with *1k* parameters","venue":null,"work_id":"3c651de4-489e-4ea3-8f63-8378f459ca1a","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.162107Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:dcfeff97cb1701ca13172cf298619a1856c68e2f76f883b837ea7d5ba058990d","observation_id":"09307584-f4d7-4dc5-aaa0-981f3ffb9fb4","resolution":{"observed_at":"2026-08-10T22:38:44.043724Z","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":"2026-08-10T22:38:43.931682Z","title":"Linearized alternating direction method with adaptive penalty for low-rank representation","venue":null,"work_id":"7f90a571-2831-4983-83e7-1b9687daf2b6","year":2011},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.172433Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:88dc37343fead507c2133e017e3f5be52b41b6e02b2a753f9a8127cb69d2280e","observation_id":"a17328d9-2705-4349-8f39-bd59f48e29af","resolution":{"observed_at":"2026-08-10T22:38:43.984632Z","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":"2026-08-10T22:38:43.919191Z","title":"Robust recovery of subspace structures by low-rank representation","venue":null,"work_id":"64b12200-54e6-4404-b4f5-4207add24be6","year":2012},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.185552Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:a6ae1c53b3688ddd6d1a30750e936ff01ca49546ac6902cf8c1650880be7865f","observation_id":"651fcf9b-600e-46c3-bb7f-5affb0f2404e","resolution":{"observed_at":"2026-08-10T22:38:43.923902Z","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":"2026-08-10T22:38:43.907475Z","title":"and Chen, S","venue":null,"work_id":"91d0ff9f-3eea-4109-99d3-02177874bbff","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.251332Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:9bc47ddc14ae6da3f75ed9b632f073f1746299f12f18947b94b77d29fdf9a7b7","observation_id":"608a94df-53a2-4135-bc79-e327f38cba93","resolution":{"observed_at":"2026-08-10T22:38:43.911253Z","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":"2026-08-10T22:38:43.895149Z","title":"Anomaly and change point detection for time series with concept drift","venue":null,"work_id":"6dae8ac0-4e1c-4010-8c3d-6059df2899fd","year":2023},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.364792Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:26fd3b8e5fa4d3f0da69952d43bdf5725bd81d029782f18443d177803a0d40af","observation_id":"8b143532-fd6d-4268-93f3-2ae1f4858a30","resolution":{"observed_at":"2026-08-10T22:38:43.900194Z","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":"2026-08-10T22:38:43.881191Z","title":"Subspace clustering by block diagonal representation","venue":null,"work_id":"11e78fdc-ca18-4c23-b992-8a3b3983c6be","year":2018},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.371544Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:31d2a782f7f9687d493a025cacfd0f5027b4bbd554b2cfe032205166c204ad4d","observation_id":"da4ca81c-9cd9-423e-83b3-ce918ad977f4","resolution":{"observed_at":"2026-08-10T22:38:43.886304Z","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":"2026-08-10T22:38:43.788627Z","title":"Learning under concept drift: A review","venue":null,"work_id":"2dec8a7c-cf1d-4eaa-bf67-3166b56ee0ce","year":2018},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.377441Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:59e4b3722816fb4293f09295a35f982ebfb113b83bf6c41d4ee40a36b6da597c","observation_id":"1c0e713f-575e-4d52-96b8-8f4ea1fbc0b0","resolution":{"observed_at":"2026-08-10T22:38:43.842063Z","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":"2304.09836","last_updated":"2023-06-06T15:39:51Z","snapshot_observed_at":"2026-08-11T06:15:15.592257Z","submitted_at":"2023-04-19T17:38:42Z","title":"Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts","version":2},"cited_work":{"arxiv_id":"2304.09836","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.09836","snapshot_observed_at":"2026-08-10T22:38:42.545814Z","title":"Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts","venue":"cs.LG","work_id":"83ac2f87-08c7-49c5-a7cf-2cb6c6e873ab","year":2023},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.404397Z"},"links":{"cited_paper":"/paper/2304.09836","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:3ebac2b45c837a72227af6aed333b7eef30ebb830ae8ad7c39acb20bce47fb45","observation_id":"c7a9be2e-8cf2-4ac7-b479-71aad71a1f6c","resolution":{"observed_at":"2026-08-10T22:38:42.590566Z","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":"2026-08-10T22:38:43.758831Z","title":"and Sakurai, Y","venue":null,"work_id":"8ae5b807-8d2c-465e-ab80-5bc24eacdf8b","year":2016},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.418429Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:f3fc77f4855f8043c4dddaead361397a258555b3d2fcb964daf810e68afd29fc","observation_id":"361b9abe-3e0f-4c94-931d-596505b05dd8","resolution":{"observed_at":"2026-08-10T22:38:43.762551Z","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":"2026-08-10T22:38:43.745695Z","title":"and Sakurai, Y","venue":null,"work_id":"07454f3d-667c-47f0-9dc6-c5eec7526970","year":2019},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.422825Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:5f5d2bba2011a4ce47a6d339e968be9ccb69b9ad1d5730b306f73d6ad37e8e22","observation_id":"41ddd0e5-d2aa-4259-aa95-25c4554f1922","resolution":{"observed_at":"2026-08-10T22:38:43.750790Z","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":"2026-08-10T22:38:43.729465Z","title":"and Kajino, H","venue":null,"work_id":"737b7116-4f3a-4ff5-9a22-781805c543c8","year":2019},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.426150Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:c8fb9a8c92423341a7a7c0e91f1c49e741f4c383d04393dee1fd27f8e1414864","observation_id":"fcf6c704-e5c1-4f7b-9bdd-7accb7d751ad","resolution":{"observed_at":"2026-08-10T22:38:43.736950Z","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":"2026-08-10T22:38:43.644334Z","title":"On spectral clustering: Analysis and an algorithm","venue":null,"work_id":"1e9ccfeb-9443-4a00-a7b7-95625a5e0a41","year":2001},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.510328Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:b343b460e7787a19d9e96df2e1a9312f3db871b2f40f8dcd1ffa54ccef30aca4","observation_id":"2a342825-8ff9-4792-865f-0ff42b5f1336","resolution":{"observed_at":"2026-08-10T22:38:43.696031Z","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":"2026-08-10T22:38:43.600250Z","title":"Clustering and projected clustering with adaptive neighbors","venue":null,"work_id":"6c0bf061-93de-416e-8c66-266de2fddff5","year":2014},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.560283Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:eb6520febbf7b2bc36135409e68d19b73e13d07b15e5e9deef4cba8b9cb8ea88","observation_id":"394c21ba-dbcd-4afc-947c-352c4a4e0c5d","resolution":{"observed_at":"2026-08-10T22:38:43.604772Z","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":"1905.10437","last_updated":"2020-02-20T21:08:57Z","snapshot_observed_at":"2026-08-10T10:26:03.789478Z","submitted_at":"2019-05-24T20:28:57Z","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10437","snapshot_observed_at":"2026-08-10T22:38:41.657029Z","title":"N., Carpov, D., Chapados, N., and Bengio, Y","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.657029Z"},"links":{"cited_paper":"/paper/1905.10437","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:663905b68f26d07e6898a3dd71aaae8898ad309050cc7ed627b0398909c15564","observation_id":"bac9bf3d-98c0-4501-9936-7deef9254124","resolution":{"observed_at":"2026-08-10T22:38:41.657029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.11672","last_updated":"2022-10-17T17:01:20Z","snapshot_observed_at":"2026-08-10T09:12:50.127426Z","submitted_at":"2022-02-23T18:23:07Z","title":"Learning Fast and Slow for Online Time Series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.11672","snapshot_observed_at":"2026-08-10T22:38:41.699217Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.699217Z"},"links":{"cited_paper":"/paper/2202.11672","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:f2de56cffea17d73ba9b23e79be4b032f0ddfb9ffd75e98d26d2a0b0ca328b7d","observation_id":"5a8381ae-fe3b-4b79-8dc4-2853c02bb81b","resolution":{"observed_at":"2026-08-10T22:38:41.699217Z","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":"2026-08-10T22:38:43.586054Z","title":"Knowledge-maximized ensemble algorithm for different types of concept drift","venue":null,"work_id":"5b4b79e5-54f8-409d-8ccf-135ee7d8afbc","year":2018},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.703330Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:6da48d9fc543609a0faf277c0795dffaeb0039311962ba96441fafaa3fac80f2","observation_id":"47ae74b0-b094-4f69-861d-124236be7cef","resolution":{"observed_at":"2026-08-10T22:38:43.591320Z","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":"2026-08-10T22:38:43.552100Z","title":"Stochastic complexity in statistical inquiry, volume 15","venue":null,"work_id":"8f3ae0fd-21b2-4203-932d-42a0595c3444","year":1998},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.707280Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:a4f3129bcf5e620509d472981510a19fd64767ef0d9c13c12722d2dedb130ec5","observation_id":"ed27552b-8b59-467c-a787-1ba8814c82d2","resolution":{"observed_at":"2026-08-10T22:38:43.577003Z","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":"2026-08-10T22:38:43.507785Z","title":null,"venue":null,"work_id":"edec85ad-a33e-4f5e-aac3-ce5d132758b9","year":1990},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.711144Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:9a00390ca922d1b7552ae86af2efe2e8933c48ebd92e9871a7bfe160f2ca35f1","observation_id":"c3dc40a6-26d3-434e-9722-14f444022a88","resolution":{"observed_at":"2026-08-10T22:38:43.511438Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:38:41.714957Z","title":"and Hinton, G","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.714957Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:5dbb345058b94a8716e0ecde831ba336139b53f5955df57f0961eb5092cf6a74","observation_id":"1048ccef-638d-4cce-8f29-e6cfc07e7326","resolution":{"observed_at":"2026-08-10T22:38:41.714957Z","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":"2026-08-10T22:38:43.488902Z","title":"A tutorial on spectral clustering","venue":null,"work_id":"a83354bd-fcc5-4529-9323-af2ed7c6ca29","year":2007},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.718619Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:175b3f660172dc130e25c84ca42cf8c97f83d8078dfaf4eae80051cf05c57ef3","observation_id":"627564a4-4233-40b4-bd87-992580c58789","resolution":{"observed_at":"2026-08-10T22:38:43.493024Z","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":"2026-08-10T22:38:43.477864Z","title":"Card: Channel aligned robust blend transformer for time series forecasting","venue":null,"work_id":"640acc0b-3e96-453a-bc52-b629eac7fb7f","year":2023},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.722853Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:6a6d44c4a3044750b2ce23c9c8de48a4fcfcca12bec69cb9ac331271f0566705","observation_id":"4154323c-bc4d-4ed8-9fee-1cfa2543fee7","resolution":{"observed_at":"2026-08-10T22:38:43.481858Z","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":"2026-08-10T22:38:43.465874Z","title":"I., Hyde, R., Cao, H., Nguyen, H","venue":null,"work_id":"7e37ef4e-a7c4-4625-8269-74878035c587","year":2016},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.726857Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:dbc8c58fceb2b969465175bfb348414d6dae3a0e12b25eff05bf2e1dee0b213f","observation_id":"6d7be8b8-10c5-48e0-b761-9e168201373b","resolution":{"observed_at":"2026-08-10T22:38:43.469810Z","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":"2026-08-10T22:38:43.445194Z","title":"Onenet: Enhancing time series forecasting models under concept drift by online ensembling","venue":null,"work_id":"ec06df87-63c3-4073-9a22-ab32dc848c0c","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.730385Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:ab3d6142edb2db42f7f9bc5fd232c31759f866b03d70ac0a8900fc977431d4ff","observation_id":"38cf3c15-9333-48ce-ade3-d443aefd34ad","resolution":{"observed_at":"2026-08-10T22:38:43.456694Z","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":"2202.01381","last_updated":"2022-06-20T06:58:05Z","snapshot_observed_at":"2026-08-10T10:48:48.098387Z","submitted_at":"2022-02-03T02:50:44Z","title":"ETSformer: Exponential Smoothing Transformers for Time-series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.01381","snapshot_observed_at":"2026-08-10T22:38:41.734079Z","title":"Etsformer: Exponential smoothing transformers for time-series forecasting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.734079Z"},"links":{"cited_paper":"/paper/2202.01381","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:71135d234bb5b220e548107e3059115aa7cc2b9f313848f9e49635cff4b535b2","observation_id":"6da4b867-7a12-4563-993a-249bb68a90ed","resolution":{"observed_at":"2026-08-10T22:38:41.734079Z","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":"2026-08-10T22:38:43.312888Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","venue":null,"work_id":"82111bb7-9685-494a-9081-67d7c1b4cc70","year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.737797Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:31e83f70187e5af3ebf92c5140310628ae84146addcf4efde306c236e80735d3","observation_id":"c08764ce-bf88-4bdf-9813-5e677b19f9e8","resolution":{"observed_at":"2026-08-10T22:38:43.358705Z","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":"2026-08-10T22:38:43.298407Z","title":"A self-representation model for robust clustering of categorical sequences","venue":null,"work_id":"8546e3a0-7234-4f06-9b8d-2ba9c9035c9b","year":2018},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.741324Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:e54a52fad82b169e063150fe985fbe6452e351e736085ac90fa4bb436b8af309","observation_id":"5bfa7a55-5357-45e8-ba25-fbbc1a5821d0","resolution":{"observed_at":"2026-08-10T22:38:43.303753Z","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":"2026-08-10T22:38:43.284733Z","title":"Kernel subspace clustering algorithm for categorical data","venue":null,"work_id":"04a3e122-d8b0-4894-96c4-aa7f1e694cb2","year":2020},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.744804Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:de63589732a61710278b6e03f0c2e0f129863ccffebe9a9ddd76f5df728b1273","observation_id":"5e7dd30d-dd2f-47c6-88c3-0e080e2f13b7","resolution":{"observed_at":"2026-08-10T22:38:43.289215Z","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":"2026-08-10T22:38:43.251556Z","title":"Data-driven kernel subspace clustering with local manifold preservation","venue":null,"work_id":"68caae5c-a482-481b-a35c-63a0b250017e","year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.748231Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:8d3dd79d103031eb10003fced104d2381648c58f3b3700bbec6076bb69e15449","observation_id":"2e180287-c3af-425d-b4c1-f6fcbf59e1ef","resolution":{"observed_at":"2026-08-10T22:38:43.274831Z","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":"2026-08-10T22:38:43.236967Z","title":"A multi-view kernel clustering framework for categorical sequences","venue":null,"work_id":"b3f6f3c3-b39c-4f6a-b543-9a6cb4d85119","year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.819050Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:226416f8830e2a4b8f83c1e715001181cbbff7dd6e8b48ff7651aee5dd43af85","observation_id":"829754f4-bd15-439e-884c-142d8c91fe64","resolution":{"observed_at":"2026-08-10T22:38:43.242306Z","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":"2026-08-10T22:38:43.223832Z","title":"Kernel representation learning with dynamic regime discovery for time series forecasting","venue":null,"work_id":"924bc1b2-743f-433a-abf8-c7244ae42883","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:41.940034Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:3f3c688a534d8649fd2bd2c98db4db057c7c1551881057a6be90c9b459603d7c","observation_id":"da50488a-a917-40e9-bbf4-3199f390d9c6","resolution":{"observed_at":"2026-08-10T22:38:43.228060Z","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":"2026-08-10T22:38:43.194189Z","title":"Rhine: A regime-switching model with nonlinear representation for discovering and forecasting regimes in financial markets","venue":null,"work_id":"f0ce407f-99b3-4d8e-8107-35cc2c079421","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.060434Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:386d4c06969c6282aeb80d890cacaa934756e4209a4304a30e5f7a2b42eff25a","observation_id":"f3e8458c-fa2e-4696-a23a-1f1719728d4a","resolution":{"observed_at":"2026-08-10T22:38:43.214016Z","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":"2026-08-10T22:38:43.081337Z","title":"Kan4drift: Are kan effective for identifying and tracking concept drift in time series? In NeurIPS Workshop on Time Series in the Age of Large Models, 2024 c","venue":null,"work_id":"29a1bd49-a08c-4d59-8d85-afdb94f47219","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.126519Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:3280ff9e7a7c4acb003e4deae7ef2f783937003c2f230073928832d59433956a","observation_id":"0ca9d6c2-839b-4ae0-9043-efb706c491de","resolution":{"observed_at":"2026-08-10T22:38:43.124758Z","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":"2406.02496","last_updated":"2024-06-04T17:14:31Z","snapshot_observed_at":"2026-08-11T00:10:54.762347Z","submitted_at":"2024-06-04T17:14:31Z","title":"Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02496","snapshot_observed_at":"2026-08-10T22:38:42.131720Z","title":"Kolmogorov-arnold networks for time series: Bridging predictive power and interpretability","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.131720Z"},"links":{"cited_paper":"/paper/2406.02496","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:8caf970f8993c175247d4ca331c1f3d184c18a30a6a037f1f73cda35540e69ef","observation_id":"2b002708-29cd-4d36-933c-e863c8d30bc2","resolution":{"observed_at":"2026-08-10T22:38:42.131720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13857","last_updated":"2024-09-20T19:11:39Z","snapshot_observed_at":"2026-07-06T19:19:02.260202Z","submitted_at":"2024-09-20T19:11:39Z","title":"Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences","version":1},"cited_work":{"arxiv_id":"2409.13857","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.13857","snapshot_observed_at":"2026-08-10T22:38:42.446339Z","title":"Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences","venue":"cs.LG","work_id":"9343a0de-9076-4a66-a2ac-a8f65af5c0e6","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.136206Z"},"links":{"cited_paper":"/paper/2409.13857","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:32ec13ffd0262aa3c7ad612d4fdb8170b7b58efd96353dc5fe2bbc0589b1d607","observation_id":"80a5bc52-88e0-49df-a63f-25e509384448","resolution":{"observed_at":"2026-08-10T22:38:42.484424Z","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":"2501.06368","last_updated":"2025-01-20T22:06:39Z","snapshot_observed_at":"2026-08-10T21:00:21.030573Z","submitted_at":"2025-01-10T22:41:02Z","title":"Towards Robust Nonlinear Subspace Clustering: A Kernel Learning Approach","version":2},"cited_work":{"arxiv_id":"2501.06368","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.06368","snapshot_observed_at":"2026-08-10T22:38:42.387174Z","title":"Towards Robust Nonlinear Subspace Clustering: A Kernel Learning Approach","venue":"cs.LG","work_id":"8deb476a-4697-49b9-bfdd-3bfa62f44aa3","year":2025},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.140037Z"},"links":{"cited_paper":"/paper/2501.06368","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:37ad2f81d86cf3617efb6000da88648d0cd6ad4b92692b374adb9d6943e694c7","observation_id":"0e492db8-7077-427d-86cb-88adac828c50","resolution":{"observed_at":"2026-08-10T22:38:42.394373Z","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":"2026-08-10T22:38:43.067879Z","title":"FITS : Modeling time series with \\ 10k\\ parameters","venue":null,"work_id":"6860ae21-ae23-49de-8dcd-928ff9fd22f7","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.144144Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:0bb6dd2a93d85543957a5c0ca3ac661916e9d876d627bab9eb22d1d9a5d28f77","observation_id":"e263da59-1a17-4662-a2cf-d39e8ba2641a","resolution":{"observed_at":"2026-08-10T22:38:43.072558Z","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":"2026-08-10T22:38:43.008648Z","title":"and Hong, S","venue":null,"work_id":"b6b1df6a-f2ff-4adc-9b57-a8dc2065e9ea","year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.148191Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:5f75db0d39894353cdbed57eb66329e5e856e257531e3ab11bdf0a682a8c4a65","observation_id":"3fb4f935-74bf-445c-85d4-fc5d15fe3dec","resolution":{"observed_at":"2026-08-10T22:38:43.057872Z","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":"2026-08-10T22:38:42.859023Z","title":"Learning to learn the future: Modeling concept drifts in time series prediction","venue":null,"work_id":"f4441a25-d22e-4782-a199-2777bf582ec2","year":2021},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.152257Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:1031fd54e40a60ec64199d6567599bcf83221073fbade132a9bf4abc63a04205","observation_id":"3d21dd8b-f593-4140-ba82-8bf2e30e26b9","resolution":{"observed_at":"2026-08-10T22:38:42.931491Z","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":"2026-08-10T22:38:42.844563Z","title":"Online boosting adaptive learning under concept drift for multistream classification","venue":null,"work_id":"004c5524-8ae1-4962-95b8-aa145a5bec5e","year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.156030Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:79f1c802e99d6fc065da65aec0df17d4a6f47d449b72bef2f3829576be66c467","observation_id":"15f66968-43e9-4d4a-8cde-d671aaa4a8a8","resolution":{"observed_at":"2026-08-10T22:38:42.849849Z","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":"2026-08-10T22:38:42.159764Z","title":"Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, pp.\\ 11121--11128, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.159764Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:f61ad4ca8711f65ce41f6726f8d88532064cde81de7f3a3ebf6806250037e6aa","observation_id":"a14d460c-5103-4dd7-b482-431003bf1f27","resolution":{"observed_at":"2026-08-10T22:38:42.159764Z","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":"2026-08-10T22:38:42.753808Z","title":"Graph structure fusion for multiview clustering","venue":null,"work_id":"d556b9bf-44c1-41cf-b230-8a3ce540b269","year":1984},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.163572Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:bf6931d5e01aa692aa07cae40881d6608dc5ca7b202f90ca3e096bded731348c","observation_id":"0d7e3a15-b376-43d1-864b-90b93eb198db","resolution":{"observed_at":"2026-08-10T22:38:42.776194Z","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":"2403.14949","last_updated":"2024-03-22T04:44:43Z","snapshot_observed_at":"2026-08-02T18:03:42.368273Z","submitted_at":"2024-03-22T04:44:43Z","title":"Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14949","snapshot_observed_at":"2026-08-10T22:38:42.167608Z","title":"Addressing concept shift in online time series forecasting: Detect-then-adapt","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.167608Z"},"links":{"cited_paper":"/paper/2403.14949","citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:ada5f7f335510d3e51b153df88037770dd03a6668da497cc258b1f76d71872c5","observation_id":"7c445a70-9651-47eb-a5b6-a45e2b137776","resolution":{"observed_at":"2026-08-10T22:38:42.167608Z","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":"2026-08-10T22:38:42.171430Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.171430Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:e122b0dad7a94505f6796d72d621238fe3a0f4dc987c8838a0c3a3066e6a8556","observation_id":"62c16976-663f-482d-b899-8191b0acabf4","resolution":{"observed_at":"2026-08-10T22:38:42.171430Z","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":"2026-08-10T22:38:42.291045Z","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.291045Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:c3ad49429464a4f8ef5bbfb68ec0ca439f16b097a213a55446f344d721dc321b","observation_id":"1ca48c6f-74a3-434e-83e5-d0e1484d97c9","resolution":{"observed_at":"2026-08-10T22:38:42.291045Z","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":"2026-08-10T22:38:42.329042Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","version":3},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:42.329042Z"},"links":{"citing_paper":"/paper/2501.01480"},"observation_digest":"sha256:5723424aaeb7223eba93c60f60387c29c3b56f8905fdc47e64d36c4326ae9589","observation_id":"0d9d9319-dd23-474d-b4c2-052c87e84f52","resolution":{"observed_at":"2026-08-10T22:38:42.329042Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.01480","last_updated":"2025-01-31T18:13:14Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T16:30:55.281006Z","submitted_at":"2025-01-02T15:09:00Z","title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series"},"reference_resolution":{"displayed":67,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":3,"verified_fuzzy":51},"total_outbound_references":67},"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 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2501.01480."}