{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:I65FRKCDPPYSOIIYKRKUOKYHUF","short_pith_number":"pith:I65FRKCD","schema_version":"1.0","canonical_sha256":"47ba58a8437bf12721185455472b07a16bfaf6f45014801c8d904cf58ec75f74","source":{"kind":"arxiv","id":"2106.07473","version":1},"attestation_state":"computed","paper":{"title":"Time Series Anomaly Detection with label-free Model Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Claudionor Nunes Coelho Jr, Deokwoo Jung, Jake Taylor, Mehrnaz Amjadi, Nandini Ramanan, Sankeerth Rao Karingula","submitted_at":"2021-06-11T00:21:06Z","abstract_excerpt":"Anomaly detection for time-series data becomes an essential task for many data-driven applications fueled with an abundance of data and out-of-the-box machine-learning algorithms. In many real-world settings, developing a reliable anomaly model is highly challenging due to insufficient anomaly labels and the prohibitively expensive cost of obtaining anomaly examples. It imposes a significant bottleneck to evaluate model quality for model selection and parameter tuning reliably. As a result, many existing anomaly detection algorithms fail to show their promised performance after deployment.\n  I"},"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":"2106.07473","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-11T00:21:06Z","cross_cats_sorted":[],"title_canon_sha256":"0af335f5640b0bb9e3cd32c3c8a3c1591452f90db0ca90cc3e2a16993b4ad527","abstract_canon_sha256":"ec31c76af100342b82c4c7c66e174b0629985c68003b060e8f8fc6a483075f1a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:49:05.550881Z","signature_b64":"FFb776wFFiWFOGY/yR8A4B4Mx6GzHhXgWSFmtxJOqCX2RBDNPsKtiKCSrJ3qUfG8hlfCjRA8NgCZ4EVSMhNdDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47ba58a8437bf12721185455472b07a16bfaf6f45014801c8d904cf58ec75f74","last_reissued_at":"2026-07-05T02:49:05.550394Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:49:05.550394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time Series Anomaly Detection with label-free Model Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Claudionor Nunes Coelho Jr, Deokwoo Jung, Jake Taylor, Mehrnaz Amjadi, Nandini Ramanan, Sankeerth Rao Karingula","submitted_at":"2021-06-11T00:21:06Z","abstract_excerpt":"Anomaly detection for time-series data becomes an essential task for many data-driven applications fueled with an abundance of data and out-of-the-box machine-learning algorithms. In many real-world settings, developing a reliable anomaly model is highly challenging due to insufficient anomaly labels and the prohibitively expensive cost of obtaining anomaly examples. It imposes a significant bottleneck to evaluate model quality for model selection and parameter tuning reliably. As a result, many existing anomaly detection algorithms fail to show their promised performance after deployment.\n  I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.07473","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/2106.07473/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":"2106.07473","created_at":"2026-07-05T02:49:05.550455+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.07473v1","created_at":"2026-07-05T02:49:05.550455+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.07473","created_at":"2026-07-05T02:49:05.550455+00:00"},{"alias_kind":"pith_short_12","alias_value":"I65FRKCDPPYS","created_at":"2026-07-05T02:49:05.550455+00:00"},{"alias_kind":"pith_short_16","alias_value":"I65FRKCDPPYSOIIY","created_at":"2026-07-05T02:49:05.550455+00:00"},{"alias_kind":"pith_short_8","alias_value":"I65FRKCD","created_at":"2026-07-05T02:49:05.550455+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.00107","citing_title":"An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF","json":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF.json","graph_json":"https://pith.science/api/pith-number/I65FRKCDPPYSOIIYKRKUOKYHUF/graph.json","events_json":"https://pith.science/api/pith-number/I65FRKCDPPYSOIIYKRKUOKYHUF/events.json","paper":"https://pith.science/paper/I65FRKCD"},"agent_actions":{"view_html":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF","download_json":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF.json","view_paper":"https://pith.science/paper/I65FRKCD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.07473&json=true","fetch_graph":"https://pith.science/api/pith-number/I65FRKCDPPYSOIIYKRKUOKYHUF/graph.json","fetch_events":"https://pith.science/api/pith-number/I65FRKCDPPYSOIIYKRKUOKYHUF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF/action/storage_attestation","attest_author":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF/action/author_attestation","sign_citation":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF/action/citation_signature","submit_replication":"https://pith.science/pith/I65FRKCDPPYSOIIYKRKUOKYHUF/action/replication_record"}},"created_at":"2026-07-05T02:49:05.550455+00:00","updated_at":"2026-07-05T02:49:05.550455+00:00"}