{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:Q2EGYLYEIH6YYR2V7D5KEMA6LQ","short_pith_number":"pith:Q2EGYLYE","schema_version":"1.0","canonical_sha256":"86886c2f0441fd8c4755f8faa2301e5c3b069a83803e7a3bfd7470ff4df89ce3","source":{"kind":"arxiv","id":"1712.08871","version":1},"attestation_state":"computed","paper":{"title":"A Data-driven Approach to Multi-event Analytics in Large-scale Power Systems Using Factor Model","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Fan Yang, Robert Caiming Qiu, Xing He, Zenan ling","submitted_at":"2017-12-24T03:22:24Z","abstract_excerpt":"Multi-event detection and recognition in real time is of challenge for a modern grid as its feature is usually non-identifiable. Based on factor model, this paper porposes a data-driven method as an alternative solution under the framework of random matrix theory. This method maps the raw data into a high-dimensional space with two parts: 1) the principal components (factors, mapping event signals); and 2) time series residuals (bulk, mapping white/non-Gaussian noises). The spatial information is extracted form factors, and the termporal infromation from residuals. Taking both spatial-tempral "},"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":"1712.08871","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"stat.AP","submitted_at":"2017-12-24T03:22:24Z","cross_cats_sorted":[],"title_canon_sha256":"952ea9d12a73ae6fad13596afafc068b37a53ce1e891efedac1b7598bcdb27b2","abstract_canon_sha256":"46531e725231cbbf2c6dc65b24481504a779f2cc0705968992837dec0c1c05c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:14.958345Z","signature_b64":"WPtkIxU5kpUm4ENFZyJQJvo+KyozyDKlHbCFmngICxD0CtK5q5PUU8wh1Zso1Pssa9pf7ShmWbXqKtzo8rL9AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86886c2f0441fd8c4755f8faa2301e5c3b069a83803e7a3bfd7470ff4df89ce3","last_reissued_at":"2026-05-18T00:27:14.957926Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:14.957926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Data-driven Approach to Multi-event Analytics in Large-scale Power Systems Using Factor Model","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Fan Yang, Robert Caiming Qiu, Xing He, Zenan ling","submitted_at":"2017-12-24T03:22:24Z","abstract_excerpt":"Multi-event detection and recognition in real time is of challenge for a modern grid as its feature is usually non-identifiable. Based on factor model, this paper porposes a data-driven method as an alternative solution under the framework of random matrix theory. This method maps the raw data into a high-dimensional space with two parts: 1) the principal components (factors, mapping event signals); and 2) time series residuals (bulk, mapping white/non-Gaussian noises). The spatial information is extracted form factors, and the termporal infromation from residuals. Taking both spatial-tempral "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.08871","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":""},"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":"1712.08871","created_at":"2026-05-18T00:27:14.957983+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.08871v1","created_at":"2026-05-18T00:27:14.957983+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.08871","created_at":"2026-05-18T00:27:14.957983+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q2EGYLYEIH6Y","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q2EGYLYEIH6YYR2V","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q2EGYLYE","created_at":"2026-05-18T12:31:37.085036+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/Q2EGYLYEIH6YYR2V7D5KEMA6LQ","json":"https://pith.science/pith/Q2EGYLYEIH6YYR2V7D5KEMA6LQ.json","graph_json":"https://pith.science/api/pith-number/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/graph.json","events_json":"https://pith.science/api/pith-number/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/events.json","paper":"https://pith.science/paper/Q2EGYLYE"},"agent_actions":{"view_html":"https://pith.science/pith/Q2EGYLYEIH6YYR2V7D5KEMA6LQ","download_json":"https://pith.science/pith/Q2EGYLYEIH6YYR2V7D5KEMA6LQ.json","view_paper":"https://pith.science/paper/Q2EGYLYE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.08871&json=true","fetch_graph":"https://pith.science/api/pith-number/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/graph.json","fetch_events":"https://pith.science/api/pith-number/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/action/storage_attestation","attest_author":"https://pith.science/pith/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/action/author_attestation","sign_citation":"https://pith.science/pith/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/action/citation_signature","submit_replication":"https://pith.science/pith/Q2EGYLYEIH6YYR2V7D5KEMA6LQ/action/replication_record"}},"created_at":"2026-05-18T00:27:14.957983+00:00","updated_at":"2026-05-18T00:27:14.957983+00:00"}