{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YEEBYEP7PHSX2YATFN7YM522Q3","short_pith_number":"pith:YEEBYEP7","schema_version":"1.0","canonical_sha256":"c1081c11ff79e57d60132b7f86775a86da957425b12487ae4ff7cade0eb63abe","source":{"kind":"arxiv","id":"2310.11169","version":1},"attestation_state":"computed","paper":{"title":"MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network 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":"Chaoyue Ding, Jing Zhao, Shiliang Sun","submitted_at":"2023-10-17T11:37:40Z","abstract_excerpt":"Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and stability of working devices (e.g., water treatment system and spacecraft), whose data are characterized by multivariate time series with diverse modalities. Although recent deep learning methods show great potential in anomaly detection, they do not explicitly capture spatial-temporal relationships between univariate time series of different modalities, resulting in more false negatives and false positives. In this paper, we propose a multimodal spatial-temporal graph attention network (MST-GAT) to tackle"},"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":"2310.11169","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-17T11:37:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c1cbb0104e3f48819fa1fc932638606c742982484abd71438fc80321a852ba77","abstract_canon_sha256":"aba431a794806d0a230ba77f230b2d0263824c29d4c37a88ee104d8e21ced164"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:51.481880Z","signature_b64":"JP0M7zwEW8nY/TcSf+0s6xBvR9Exb74uj+2DyZ9Efuc5bAQLIiDYEqDZTyBrAFH7iI66OR1DJ+49zxOPbXYWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1081c11ff79e57d60132b7f86775a86da957425b12487ae4ff7cade0eb63abe","last_reissued_at":"2026-07-05T07:01:51.481411Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:51.481411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network 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":"Chaoyue Ding, Jing Zhao, Shiliang Sun","submitted_at":"2023-10-17T11:37:40Z","abstract_excerpt":"Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and stability of working devices (e.g., water treatment system and spacecraft), whose data are characterized by multivariate time series with diverse modalities. Although recent deep learning methods show great potential in anomaly detection, they do not explicitly capture spatial-temporal relationships between univariate time series of different modalities, resulting in more false negatives and false positives. In this paper, we propose a multimodal spatial-temporal graph attention network (MST-GAT) to tackle"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.11169","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/2310.11169/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":"2310.11169","created_at":"2026-07-05T07:01:51.481473+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.11169v1","created_at":"2026-07-05T07:01:51.481473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.11169","created_at":"2026-07-05T07:01:51.481473+00:00"},{"alias_kind":"pith_short_12","alias_value":"YEEBYEP7PHSX","created_at":"2026-07-05T07:01:51.481473+00:00"},{"alias_kind":"pith_short_16","alias_value":"YEEBYEP7PHSX2YAT","created_at":"2026-07-05T07:01:51.481473+00:00"},{"alias_kind":"pith_short_8","alias_value":"YEEBYEP7","created_at":"2026-07-05T07:01:51.481473+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.25515","citing_title":"Spatiotemporal Forecasting of Incidents and Congestion with Implications for Sustainable Traffic Control","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3","json":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3.json","graph_json":"https://pith.science/api/pith-number/YEEBYEP7PHSX2YATFN7YM522Q3/graph.json","events_json":"https://pith.science/api/pith-number/YEEBYEP7PHSX2YATFN7YM522Q3/events.json","paper":"https://pith.science/paper/YEEBYEP7"},"agent_actions":{"view_html":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3","download_json":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3.json","view_paper":"https://pith.science/paper/YEEBYEP7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.11169&json=true","fetch_graph":"https://pith.science/api/pith-number/YEEBYEP7PHSX2YATFN7YM522Q3/graph.json","fetch_events":"https://pith.science/api/pith-number/YEEBYEP7PHSX2YATFN7YM522Q3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3/action/storage_attestation","attest_author":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3/action/author_attestation","sign_citation":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3/action/citation_signature","submit_replication":"https://pith.science/pith/YEEBYEP7PHSX2YATFN7YM522Q3/action/replication_record"}},"created_at":"2026-07-05T07:01:51.481473+00:00","updated_at":"2026-07-05T07:01:51.481473+00:00"}