{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:EH4GWH3PPNDYPZPFP34CNUXA32","short_pith_number":"pith:EH4GWH3P","canonical_record":{"source":{"id":"2406.19770","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-28T09:17:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9b8f1ed7187def34296b4180d970d6b6472fb6324f3610160bbb3c8beee34ab1","abstract_canon_sha256":"e8167bce152c361a727b8ed21b0a5b75dabc6ad55c28d7651cc3e53e634b3476"},"schema_version":"1.0"},"canonical_sha256":"21f86b1f6f7b4787e5e57ef826d2e0dea5e42ad2f205b9780822c23a402c6d48","source":{"kind":"arxiv","id":"2406.19770","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.19770","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"arxiv_version","alias_value":"2406.19770v1","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.19770","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"pith_short_12","alias_value":"EH4GWH3PPNDY","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"pith_short_16","alias_value":"EH4GWH3PPNDYPZPF","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"pith_short_8","alias_value":"EH4GWH3P","created_at":"2026-07-05T08:37:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:EH4GWH3PPNDYPZPFP34CNUXA32","target":"record","payload":{"canonical_record":{"source":{"id":"2406.19770","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-28T09:17:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9b8f1ed7187def34296b4180d970d6b6472fb6324f3610160bbb3c8beee34ab1","abstract_canon_sha256":"e8167bce152c361a727b8ed21b0a5b75dabc6ad55c28d7651cc3e53e634b3476"},"schema_version":"1.0"},"canonical_sha256":"21f86b1f6f7b4787e5e57ef826d2e0dea5e42ad2f205b9780822c23a402c6d48","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:37:54.753471Z","signature_b64":"mG3CSCUVA//Il1sTNYzEq1DyOcXVkyQfrdv3o1bNDFzw8QnrLocjJkWGC4VoXfkMaK5KJ+Gtx0XNWo1HdfZMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21f86b1f6f7b4787e5e57ef826d2e0dea5e42ad2f205b9780822c23a402c6d48","last_reissued_at":"2026-07-05T08:37:54.753044Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:37:54.753044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.19770","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:37:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wpcrLX3KLjvXkgcMRgG81mumkBs/b3HghHxrv7aI7a2H3XuNUhM1vMxFOo6WMpUdaWaBbjdbS01LvXgOXLyDAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T22:44:17.464894Z"},"content_sha256":"a7d3574d1c1d039df2d20de76a28391496bd397e03dd9b86c96faa5569b3a404","schema_version":"1.0","event_id":"sha256:a7d3574d1c1d039df2d20de76a28391496bd397e03dd9b86c96faa5569b3a404"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:EH4GWH3PPNDYPZPFP34CNUXA32","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Guansong Pang, Hezhe Qiao, Hongzuo Xu, Mingsheng Shang, Yuan Zhou, Yutong Chen","submitted_at":"2024-06-28T09:17:58Z","abstract_excerpt":"Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normalit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.19770","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/2406.19770/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:37:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uiITj6gnL8IH+/tpOxa1IdC1I/7Jq0mCVXifD63Ub0r+vLursV2HNqZNWdUXdEru+ibGIs1cnDD0PUzSx3q1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T22:44:17.465424Z"},"content_sha256":"f65a134d2b30991fab0a34dda4a8eb8e31c88e3a00f234de67803f7d15d40705","schema_version":"1.0","event_id":"sha256:f65a134d2b30991fab0a34dda4a8eb8e31c88e3a00f234de67803f7d15d40705"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EH4GWH3PPNDYPZPFP34CNUXA32/bundle.json","state_url":"https://pith.science/pith/EH4GWH3PPNDYPZPFP34CNUXA32/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EH4GWH3PPNDYPZPFP34CNUXA32/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T22:44:17Z","links":{"resolver":"https://pith.science/pith/EH4GWH3PPNDYPZPFP34CNUXA32","bundle":"https://pith.science/pith/EH4GWH3PPNDYPZPFP34CNUXA32/bundle.json","state":"https://pith.science/pith/EH4GWH3PPNDYPZPFP34CNUXA32/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EH4GWH3PPNDYPZPFP34CNUXA32/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EH4GWH3PPNDYPZPFP34CNUXA32","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":"e8167bce152c361a727b8ed21b0a5b75dabc6ad55c28d7651cc3e53e634b3476","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-28T09:17:58Z","title_canon_sha256":"9b8f1ed7187def34296b4180d970d6b6472fb6324f3610160bbb3c8beee34ab1"},"schema_version":"1.0","source":{"id":"2406.19770","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.19770","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"arxiv_version","alias_value":"2406.19770v1","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.19770","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"pith_short_12","alias_value":"EH4GWH3PPNDY","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"pith_short_16","alias_value":"EH4GWH3PPNDYPZPF","created_at":"2026-07-05T08:37:54Z"},{"alias_kind":"pith_short_8","alias_value":"EH4GWH3P","created_at":"2026-07-05T08:37:54Z"}],"graph_snapshots":[{"event_id":"sha256:f65a134d2b30991fab0a34dda4a8eb8e31c88e3a00f234de67803f7d15d40705","target":"graph","created_at":"2026-07-05T08:37:54Z","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/2406.19770/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normalit","authors_text":"Guansong Pang, Hezhe Qiao, Hongzuo Xu, Mingsheng Shang, Yuan Zhou, Yutong Chen","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-28T09:17:58Z","title":"Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.19770","kind":"arxiv","version":1},"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:a7d3574d1c1d039df2d20de76a28391496bd397e03dd9b86c96faa5569b3a404","target":"record","created_at":"2026-07-05T08:37:54Z","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":"e8167bce152c361a727b8ed21b0a5b75dabc6ad55c28d7651cc3e53e634b3476","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-28T09:17:58Z","title_canon_sha256":"9b8f1ed7187def34296b4180d970d6b6472fb6324f3610160bbb3c8beee34ab1"},"schema_version":"1.0","source":{"id":"2406.19770","kind":"arxiv","version":1}},"canonical_sha256":"21f86b1f6f7b4787e5e57ef826d2e0dea5e42ad2f205b9780822c23a402c6d48","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21f86b1f6f7b4787e5e57ef826d2e0dea5e42ad2f205b9780822c23a402c6d48","first_computed_at":"2026-07-05T08:37:54.753044Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:37:54.753044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mG3CSCUVA//Il1sTNYzEq1DyOcXVkyQfrdv3o1bNDFzw8QnrLocjJkWGC4VoXfkMaK5KJ+Gtx0XNWo1HdfZMDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:37:54.753471Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.19770","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a7d3574d1c1d039df2d20de76a28391496bd397e03dd9b86c96faa5569b3a404","sha256:f65a134d2b30991fab0a34dda4a8eb8e31c88e3a00f234de67803f7d15d40705"],"state_sha256":"d3d1dd6e5c4cc633fbf6c315e22fc8b925ed360460ba5a8d55bf54be46bdb39f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BIgIsAlLJ3BLPpRXlIAm1RCGTSxwiSyr5esbboOwlbvXzGV/oDi66E1vZHyUM3cmC1o6zX5Bwtwke+7/M/EiAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T22:44:17.470170Z","bundle_sha256":"cbc579e3f19d622d691e6541145495c5ff09a97cc940fe7d81350e517e6e5c38"}}