{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:USBOLEIKF4VOWXRNEWHC2O7ZL3","short_pith_number":"pith:USBOLEIK","schema_version":"1.0","canonical_sha256":"a482e5910a2f2aeb5e2d258e2d3bf95ef3676b1304653a47e6d62e622e948c6c","source":{"kind":"arxiv","id":"2606.01300","version":1},"attestation_state":"computed","paper":{"title":"ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Francesco Biondani, Francesco Setti, Franco Fummi, Luigi Capogrosso, Marco Cristani, Michele Magno, Uzair Khan","submitted_at":"2026-05-31T15:42:40Z","abstract_excerpt":"Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across different datasets, especially when anomalies are subtle or context-dependent. To solve this issue, we introduce ChronosAD, a novel architecture for anomaly detection that uses a time series foundation model as a feature extractor. Specifically, it employs a two-stage pipeline: first, it uses the foundation model to extract embeddings for each time series in a zero-shot manner. Then, a custom-developed Temporal Block, com"},"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":"2606.01300","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-31T15:42:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"35172d49fc99e1733e6179be48cc8cb44b76a9afdd202af007a3f73a587f7bd8","abstract_canon_sha256":"f84e08092c249860eaf9c31572e5e5c0fc666031916e597a7d5a72b432be0539"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:29.482280Z","signature_b64":"QINPlBvyizsj/8xN26rcwdxOiAZPHo2A+WiTn3fHKFbnpt2y5Xj1LLRDh5qotHm/aza5BwtF6fIW/X2pS99ADg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a482e5910a2f2aeb5e2d258e2d3bf95ef3676b1304653a47e6d62e622e948c6c","last_reissued_at":"2026-06-02T02:04:29.481861Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:29.481861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Francesco Biondani, Francesco Setti, Franco Fummi, Luigi Capogrosso, Marco Cristani, Michele Magno, Uzair Khan","submitted_at":"2026-05-31T15:42:40Z","abstract_excerpt":"Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across different datasets, especially when anomalies are subtle or context-dependent. To solve this issue, we introduce ChronosAD, a novel architecture for anomaly detection that uses a time series foundation model as a feature extractor. Specifically, it employs a two-stage pipeline: first, it uses the foundation model to extract embeddings for each time series in a zero-shot manner. Then, a custom-developed Temporal Block, com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01300","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/2606.01300/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":"2606.01300","created_at":"2026-06-02T02:04:29.481924+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.01300v1","created_at":"2026-06-02T02:04:29.481924+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01300","created_at":"2026-06-02T02:04:29.481924+00:00"},{"alias_kind":"pith_short_12","alias_value":"USBOLEIKF4VO","created_at":"2026-06-02T02:04:29.481924+00:00"},{"alias_kind":"pith_short_16","alias_value":"USBOLEIKF4VOWXRN","created_at":"2026-06-02T02:04:29.481924+00:00"},{"alias_kind":"pith_short_8","alias_value":"USBOLEIK","created_at":"2026-06-02T02:04:29.481924+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/USBOLEIKF4VOWXRNEWHC2O7ZL3","json":"https://pith.science/pith/USBOLEIKF4VOWXRNEWHC2O7ZL3.json","graph_json":"https://pith.science/api/pith-number/USBOLEIKF4VOWXRNEWHC2O7ZL3/graph.json","events_json":"https://pith.science/api/pith-number/USBOLEIKF4VOWXRNEWHC2O7ZL3/events.json","paper":"https://pith.science/paper/USBOLEIK"},"agent_actions":{"view_html":"https://pith.science/pith/USBOLEIKF4VOWXRNEWHC2O7ZL3","download_json":"https://pith.science/pith/USBOLEIKF4VOWXRNEWHC2O7ZL3.json","view_paper":"https://pith.science/paper/USBOLEIK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.01300&json=true","fetch_graph":"https://pith.science/api/pith-number/USBOLEIKF4VOWXRNEWHC2O7ZL3/graph.json","fetch_events":"https://pith.science/api/pith-number/USBOLEIKF4VOWXRNEWHC2O7ZL3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/USBOLEIKF4VOWXRNEWHC2O7ZL3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/USBOLEIKF4VOWXRNEWHC2O7ZL3/action/storage_attestation","attest_author":"https://pith.science/pith/USBOLEIKF4VOWXRNEWHC2O7ZL3/action/author_attestation","sign_citation":"https://pith.science/pith/USBOLEIKF4VOWXRNEWHC2O7ZL3/action/citation_signature","submit_replication":"https://pith.science/pith/USBOLEIKF4VOWXRNEWHC2O7ZL3/action/replication_record"}},"created_at":"2026-06-02T02:04:29.481924+00:00","updated_at":"2026-06-02T02:04:29.481924+00:00"}