{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TM6IG5WMYI2Q6H5RXQH3KYZTNA","short_pith_number":"pith:TM6IG5WM","schema_version":"1.0","canonical_sha256":"9b3c8376ccc2350f1fb1bc0fb563336835e51b161c9879d204cc1999d7c0481c","source":{"kind":"arxiv","id":"2202.11672","version":2},"attestation_state":"computed","paper":{"title":"Learning Fast and Slow for Online Time Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chenghao Liu, Doyen Sahoo, Quang Pham, Steven C.H. Hoi","submitted_at":"2022-02-23T18:23:07Z","abstract_excerpt":"The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes to new and recurring patterns. However, training deep neural forecaster on the fly is notoriously challenging because of their limited ability to adapt to non-stationary environments and the catastrophic forgetting of old knowledge. In this work, inspired by the Complementary Learning Systems (CLS) theory, we propose Fast and Slow learning Networks (FSNet), a holistic framework for online time-series forecasting to s"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2202.11672","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-23T18:23:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0f038622dbe69eca63c240b6da628b05bf6d8796063ec289e91d2d3ce47140e9","abstract_canon_sha256":"e255ebda04756772e49ae1a1756c37feea3956502b43b059d8377db879f723a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:06:55.223290Z","signature_b64":"PbLbiGfmKbbsr2n8rXZMjq0671Yz1B/Xdn2ZlJK+KTG9eFUKoBwKApRSTE3nw9M4JOgEFNyBX3f1gQYLcii1Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b3c8376ccc2350f1fb1bc0fb563336835e51b161c9879d204cc1999d7c0481c","last_reissued_at":"2026-07-05T05:06:55.222833Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:06:55.222833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Fast and Slow for Online Time Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chenghao Liu, Doyen Sahoo, Quang Pham, Steven C.H. Hoi","submitted_at":"2022-02-23T18:23:07Z","abstract_excerpt":"The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes to new and recurring patterns. However, training deep neural forecaster on the fly is notoriously challenging because of their limited ability to adapt to non-stationary environments and the catastrophic forgetting of old knowledge. In this work, inspired by the Complementary Learning Systems (CLS) theory, we propose Fast and Slow learning Networks (FSNet), a holistic framework for online time-series forecasting to s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.11672","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2202.11672/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2202.11672","created_at":"2026-07-05T05:06:55.222890+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.11672v2","created_at":"2026-07-05T05:06:55.222890+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.11672","created_at":"2026-07-05T05:06:55.222890+00:00"},{"alias_kind":"pith_short_12","alias_value":"TM6IG5WMYI2Q","created_at":"2026-07-05T05:06:55.222890+00:00"},{"alias_kind":"pith_short_16","alias_value":"TM6IG5WMYI2Q6H5R","created_at":"2026-07-05T05:06:55.222890+00:00"},{"alias_kind":"pith_short_8","alias_value":"TM6IG5WM","created_at":"2026-07-05T05:06:55.222890+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.28603","citing_title":"Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA","json":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA.json","graph_json":"https://pith.science/api/pith-number/TM6IG5WMYI2Q6H5RXQH3KYZTNA/graph.json","events_json":"https://pith.science/api/pith-number/TM6IG5WMYI2Q6H5RXQH3KYZTNA/events.json","paper":"https://pith.science/paper/TM6IG5WM"},"agent_actions":{"view_html":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA","download_json":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA.json","view_paper":"https://pith.science/paper/TM6IG5WM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.11672&json=true","fetch_graph":"https://pith.science/api/pith-number/TM6IG5WMYI2Q6H5RXQH3KYZTNA/graph.json","fetch_events":"https://pith.science/api/pith-number/TM6IG5WMYI2Q6H5RXQH3KYZTNA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA/action/storage_attestation","attest_author":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA/action/author_attestation","sign_citation":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA/action/citation_signature","submit_replication":"https://pith.science/pith/TM6IG5WMYI2Q6H5RXQH3KYZTNA/action/replication_record"}},"created_at":"2026-07-05T05:06:55.222890+00:00","updated_at":"2026-07-05T05:06:55.222890+00:00"}