{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7AG4IS564LI3B3LT2WZ4P56AVO","short_pith_number":"pith:7AG4IS56","schema_version":"1.0","canonical_sha256":"f80dc44bbee2d1b0ed73d5b3c7f7c0abb1958f9c7fac6952ff3c58292e913075","source":{"kind":"arxiv","id":"2412.19286","version":1},"attestation_state":"computed","paper":{"title":"Time Series Foundational Models: Their Role in Anomaly Detection and Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ahan Bhatt, Alaa Al Ghazo, Amit Sheth, Chathurangi Shyalika, Harleen Kaur Bagga, Renjith Prasad","submitted_at":"2024-12-26T17:15:30Z","abstract_excerpt":"Time series foundational models (TSFM) have gained prominence in time series forecasting, promising state-of-the-art performance across various applications. However, their application in anomaly detection and prediction remains underexplored, with growing concerns regarding their black-box nature, lack of interpretability and applicability. This paper critically evaluates the efficacy of TSFM in anomaly detection and prediction tasks. We systematically analyze TSFM across multiple datasets, including those characterized by the absence of discernible patterns, trends and seasonality. Our analy"},"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":"2412.19286","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-26T17:15:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"71b4c43119f89325bd51615fcaea2271256f5cbdf0c2b562a0af683427c065d6","abstract_canon_sha256":"fff7853fe13a78feab5ff21e19f7e2da12f09d65898134bf8cd2aa9aeffbef16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:33.371342Z","signature_b64":"5kTti1/w+hx2Zrxsu6kLxNczmdUoDYP22Ten9x+fmgDyvlntbI+3cHRGhoyJGM8CodiAKQ8zYjb0VpJydzriDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f80dc44bbee2d1b0ed73d5b3c7f7c0abb1958f9c7fac6952ff3c58292e913075","last_reissued_at":"2026-07-05T09:54:33.370940Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:33.370940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time Series Foundational Models: Their Role in Anomaly Detection and Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ahan Bhatt, Alaa Al Ghazo, Amit Sheth, Chathurangi Shyalika, Harleen Kaur Bagga, Renjith Prasad","submitted_at":"2024-12-26T17:15:30Z","abstract_excerpt":"Time series foundational models (TSFM) have gained prominence in time series forecasting, promising state-of-the-art performance across various applications. However, their application in anomaly detection and prediction remains underexplored, with growing concerns regarding their black-box nature, lack of interpretability and applicability. This paper critically evaluates the efficacy of TSFM in anomaly detection and prediction tasks. We systematically analyze TSFM across multiple datasets, including those characterized by the absence of discernible patterns, trends and seasonality. Our analy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19286","kind":"arxiv","version":1},"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/2412.19286/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":"2412.19286","created_at":"2026-07-05T09:54:33.370996+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.19286v1","created_at":"2026-07-05T09:54:33.370996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19286","created_at":"2026-07-05T09:54:33.370996+00:00"},{"alias_kind":"pith_short_12","alias_value":"7AG4IS564LI3","created_at":"2026-07-05T09:54:33.370996+00:00"},{"alias_kind":"pith_short_16","alias_value":"7AG4IS564LI3B3LT","created_at":"2026-07-05T09:54:33.370996+00:00"},{"alias_kind":"pith_short_8","alias_value":"7AG4IS56","created_at":"2026-07-05T09:54:33.370996+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/7AG4IS564LI3B3LT2WZ4P56AVO","json":"https://pith.science/pith/7AG4IS564LI3B3LT2WZ4P56AVO.json","graph_json":"https://pith.science/api/pith-number/7AG4IS564LI3B3LT2WZ4P56AVO/graph.json","events_json":"https://pith.science/api/pith-number/7AG4IS564LI3B3LT2WZ4P56AVO/events.json","paper":"https://pith.science/paper/7AG4IS56"},"agent_actions":{"view_html":"https://pith.science/pith/7AG4IS564LI3B3LT2WZ4P56AVO","download_json":"https://pith.science/pith/7AG4IS564LI3B3LT2WZ4P56AVO.json","view_paper":"https://pith.science/paper/7AG4IS56","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.19286&json=true","fetch_graph":"https://pith.science/api/pith-number/7AG4IS564LI3B3LT2WZ4P56AVO/graph.json","fetch_events":"https://pith.science/api/pith-number/7AG4IS564LI3B3LT2WZ4P56AVO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7AG4IS564LI3B3LT2WZ4P56AVO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7AG4IS564LI3B3LT2WZ4P56AVO/action/storage_attestation","attest_author":"https://pith.science/pith/7AG4IS564LI3B3LT2WZ4P56AVO/action/author_attestation","sign_citation":"https://pith.science/pith/7AG4IS564LI3B3LT2WZ4P56AVO/action/citation_signature","submit_replication":"https://pith.science/pith/7AG4IS564LI3B3LT2WZ4P56AVO/action/replication_record"}},"created_at":"2026-07-05T09:54:33.370996+00:00","updated_at":"2026-07-05T09:54:33.370996+00:00"}