{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:XUQ6RRMXL4AVEDWEWBJH4KNZME","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4dc9409a7ee40a09482c3141ee34e97b45b4848f87e648915a98e9dafbe6cc91","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-03T13:17:38Z","title_canon_sha256":"94454701e8bafd52f2b492fb7f4f30b18dd250b97e790677d6e3ca5e53481eee"},"schema_version":"1.0","source":{"id":"2202.01575","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.01575","created_at":"2026-07-05T04:20:32Z"},{"alias_kind":"arxiv_version","alias_value":"2202.01575v3","created_at":"2026-07-05T04:20:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.01575","created_at":"2026-07-05T04:20:32Z"},{"alias_kind":"pith_short_12","alias_value":"XUQ6RRMXL4AV","created_at":"2026-07-05T04:20:32Z"},{"alias_kind":"pith_short_16","alias_value":"XUQ6RRMXL4AVEDWE","created_at":"2026-07-05T04:20:32Z"},{"alias_kind":"pith_short_8","alias_value":"XUQ6RRMX","created_at":"2026-07-05T04:20:32Z"}],"graph_snapshots":[{"event_id":"sha256:2fb70f42fe3cb04eb4d6007b99aea081cc9382569b380b23be1c70a507474a02","target":"graph","created_at":"2026-07-05T04:20:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2202.01575/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective. Following this princi","authors_text":"Akshat Kumar, Chenghao Liu, Doyen Sahoo, Gerald Woo, Steven Hoi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-03T13:17:38Z","title":"CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.01575","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:350b990b2ebe2a8957bc56df0c2213c0e336776e02d0ea4d2dbd88ac22c31c08","target":"record","created_at":"2026-07-05T04:20:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4dc9409a7ee40a09482c3141ee34e97b45b4848f87e648915a98e9dafbe6cc91","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-03T13:17:38Z","title_canon_sha256":"94454701e8bafd52f2b492fb7f4f30b18dd250b97e790677d6e3ca5e53481eee"},"schema_version":"1.0","source":{"id":"2202.01575","kind":"arxiv","version":3}},"canonical_sha256":"bd21e8c5975f01520ec4b0527e29b9612589002044dec43df10dd724bdc4663a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bd21e8c5975f01520ec4b0527e29b9612589002044dec43df10dd724bdc4663a","first_computed_at":"2026-07-05T04:20:32.972267Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:20:32.972267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lmJ2Xp0iiQ4GXv1BOaNr87Wfm5J47mM3kdhhM5p/b8AWENamM/nrd7/EdK/7wgWHIE403oSWZR1NtBqeiQMUBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:20:32.972779Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.01575","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:350b990b2ebe2a8957bc56df0c2213c0e336776e02d0ea4d2dbd88ac22c31c08","sha256:2fb70f42fe3cb04eb4d6007b99aea081cc9382569b380b23be1c70a507474a02"],"state_sha256":"4879091213a0179ae4012dadf27abcc1454f5c41213f59bd5db81770e3f2ee5c"}