{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FTWAS5Y3USSGA2NZNEYPVLWXB7","short_pith_number":"pith:FTWAS5Y3","schema_version":"1.0","canonical_sha256":"2cec09771ba4a46069b96930faaed70fe459f4ce28f86e680c7c2f36f4c9536d","source":{"kind":"arxiv","id":"2311.11235","version":2},"attestation_state":"computed","paper":{"title":"Unraveling the \"Anomaly\" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Guanhua Ye, Guansong Pang, Hongzhi Yin, Tong Chen, Xia Hu, Yuting Sun","submitted_at":"2023-11-19T05:37:18Z","abstract_excerpt":"The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution. As limited anomaly labels hinder traditional supervised models in TSAD, various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of hav"},"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":"2311.11235","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-19T05:37:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3c99cfd874de30038ad23df168d9b5f41bcea716b837b50ebefd126bc1214e30","abstract_canon_sha256":"8a1aec5826427d8b61ccfd7610a807f0820d620851d3bd25155e9fa757436f74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:16:50.444159Z","signature_b64":"E9lX1O6W4vc5mv0i3UcJ97U6qrG8v/UbC6M14ehMWadqhAxsQT/Rh4s+ZMdUHsQZprVZgcwRVCuHXEatW+4DDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cec09771ba4a46069b96930faaed70fe459f4ce28f86e680c7c2f36f4c9536d","last_reissued_at":"2026-07-05T07:16:50.443709Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:16:50.443709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unraveling the \"Anomaly\" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Guanhua Ye, Guansong Pang, Hongzhi Yin, Tong Chen, Xia Hu, Yuting Sun","submitted_at":"2023-11-19T05:37:18Z","abstract_excerpt":"The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution. As limited anomaly labels hinder traditional supervised models in TSAD, various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of hav"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.11235","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/2311.11235/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":"2311.11235","created_at":"2026-07-05T07:16:50.443766+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.11235v2","created_at":"2026-07-05T07:16:50.443766+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.11235","created_at":"2026-07-05T07:16:50.443766+00:00"},{"alias_kind":"pith_short_12","alias_value":"FTWAS5Y3USSG","created_at":"2026-07-05T07:16:50.443766+00:00"},{"alias_kind":"pith_short_16","alias_value":"FTWAS5Y3USSGA2NZ","created_at":"2026-07-05T07:16:50.443766+00:00"},{"alias_kind":"pith_short_8","alias_value":"FTWAS5Y3","created_at":"2026-07-05T07:16:50.443766+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7","json":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7.json","graph_json":"https://pith.science/api/pith-number/FTWAS5Y3USSGA2NZNEYPVLWXB7/graph.json","events_json":"https://pith.science/api/pith-number/FTWAS5Y3USSGA2NZNEYPVLWXB7/events.json","paper":"https://pith.science/paper/FTWAS5Y3"},"agent_actions":{"view_html":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7","download_json":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7.json","view_paper":"https://pith.science/paper/FTWAS5Y3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.11235&json=true","fetch_graph":"https://pith.science/api/pith-number/FTWAS5Y3USSGA2NZNEYPVLWXB7/graph.json","fetch_events":"https://pith.science/api/pith-number/FTWAS5Y3USSGA2NZNEYPVLWXB7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7/action/storage_attestation","attest_author":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7/action/author_attestation","sign_citation":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7/action/citation_signature","submit_replication":"https://pith.science/pith/FTWAS5Y3USSGA2NZNEYPVLWXB7/action/replication_record"}},"created_at":"2026-07-05T07:16:50.443766+00:00","updated_at":"2026-07-05T07:16:50.443766+00:00"}