{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:37RIFPWZ4BAAU5ZFZNUGYZCJKN","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":"1b96a2b39eac0b4af2efcb1dc9c8d00f38537077be1fa6fe527b2af14509c2ee","cross_cats_sorted":["cs.AI","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-27T03:17:26Z","title_canon_sha256":"d18d285f4693ecb9f22f4036f7e166fcbf03c93c8fcfe96893ea1c23a2b1ad21"},"schema_version":"1.0","source":{"id":"2412.19423","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.19423","created_at":"2026-07-05T09:54:35Z"},{"alias_kind":"arxiv_version","alias_value":"2412.19423v1","created_at":"2026-07-05T09:54:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19423","created_at":"2026-07-05T09:54:35Z"},{"alias_kind":"pith_short_12","alias_value":"37RIFPWZ4BAA","created_at":"2026-07-05T09:54:35Z"},{"alias_kind":"pith_short_16","alias_value":"37RIFPWZ4BAAU5ZF","created_at":"2026-07-05T09:54:35Z"},{"alias_kind":"pith_short_8","alias_value":"37RIFPWZ","created_at":"2026-07-05T09:54:35Z"}],"graph_snapshots":[{"event_id":"sha256:19647aaf2ddcd281cbe97d8b72248f1dba286f153979e729c239a1c6bf4f8e6c","target":"graph","created_at":"2026-07-05T09:54:35Z","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/2412.19423/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Revisiting PCA for Time Series Reduction in Temporal Dimension; Jiaxin Gao, Wenbo Hu, Yuntian Chen; Deep learning has significantly advanced time series analysis (TSA), enabling the extraction of complex patterns for tasks like classification, forecasting, and regression. Although dimensionality reduction has traditionally focused on the variable space-achieving notable success in minimizing data redundancy and computational complexity-less attention has been paid to reducing the temporal dimension. In this study, we revisit Principal Component Analysis (PCA), a classical dimensionality reduct","authors_text":"Jiaxin Gao, Wenbo Hu, Yuntian Chen","cross_cats":["cs.AI","stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-27T03:17:26Z","title":"Revisiting PCA for time series reduction in temporal dimension"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19423","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":"sha256:48b70ae5199dbbca94009bf64b0c3f948b12261d17fe27733a50052ba36cd989","target":"record","created_at":"2026-07-05T09:54:35Z","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":"1b96a2b39eac0b4af2efcb1dc9c8d00f38537077be1fa6fe527b2af14509c2ee","cross_cats_sorted":["cs.AI","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-27T03:17:26Z","title_canon_sha256":"d18d285f4693ecb9f22f4036f7e166fcbf03c93c8fcfe96893ea1c23a2b1ad21"},"schema_version":"1.0","source":{"id":"2412.19423","kind":"arxiv","version":1}},"canonical_sha256":"dfe282bed9e0400a7725cb686c6449534533274015df19f122cd5e90c078afd4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dfe282bed9e0400a7725cb686c6449534533274015df19f122cd5e90c078afd4","first_computed_at":"2026-07-05T09:54:35.378232Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:54:35.378232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/oJ3T9zi82XUU6q0NTMYelWlXPCo/HagTxpVdxDfWLBFR9gsRgAZ5D5eYbtZOhaaY0uEQ9Heh94vahHWcwNHAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:54:35.378657Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.19423","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:48b70ae5199dbbca94009bf64b0c3f948b12261d17fe27733a50052ba36cd989","sha256:19647aaf2ddcd281cbe97d8b72248f1dba286f153979e729c239a1c6bf4f8e6c"],"state_sha256":"3674211f6fcf76824e3ab21ab214d7c3cf906cdf35210b1e99df6cc968dd9f62"}