{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PEMQ3GCKBTJX6CBZPB3EQSKNAF","short_pith_number":"pith:PEMQ3GCK","schema_version":"1.0","canonical_sha256":"79190d984a0cd37f0839787648494d017a2444bed9d4591e1450d81b0be2507a","source":{"kind":"arxiv","id":"2309.01161","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY","stat.ME"],"primary_cat":"math.OC","authors_text":"Jiaxin Yu, S. Joe Qin, Yanfang Mo","submitted_at":"2023-09-03T12:44:43Z","abstract_excerpt":"In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model with oblique projections. This model partitions the measurement space into a dynamic subspace and a static subspace that do not need to be orthogonal. The partition allows us to apply an oblique projection to extract dynamic latent variables (DLVs) from high-dimensional data with maximized predictability. We develop an alternating iterative PredVAR algorithm that exploits the interaction between updating the latent VAR dynamics and estimating the oblique projection, using expectation maximizatio"},"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":"2309.01161","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2023-09-03T12:44:43Z","cross_cats_sorted":["cs.SY","eess.SY","stat.ME"],"title_canon_sha256":"7a1fdc0457f5555c859bd5c26c410f56b1fb935b07225b0f065f6e7e05da0863","abstract_canon_sha256":"921e8ca2dad48abdbe195ed262570309ea526c28bdf2b0c7e3a95ed30029f5ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:47:33.688387Z","signature_b64":"thj4QZMwJOmDNR8/SehpFFyM0fo4O7q4McvG4DarUBXpsNBzkYHybouArzNqfyYEVHeVH8hDmWmphkHOd3t9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79190d984a0cd37f0839787648494d017a2444bed9d4591e1450d81b0be2507a","last_reissued_at":"2026-07-05T06:47:33.687883Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:47:33.687883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY","stat.ME"],"primary_cat":"math.OC","authors_text":"Jiaxin Yu, S. Joe Qin, Yanfang Mo","submitted_at":"2023-09-03T12:44:43Z","abstract_excerpt":"In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model with oblique projections. This model partitions the measurement space into a dynamic subspace and a static subspace that do not need to be orthogonal. The partition allows us to apply an oblique projection to extract dynamic latent variables (DLVs) from high-dimensional data with maximized predictability. We develop an alternating iterative PredVAR algorithm that exploits the interaction between updating the latent VAR dynamics and estimating the oblique projection, using expectation maximizatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.01161","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/2309.01161/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":"2309.01161","created_at":"2026-07-05T06:47:33.687942+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.01161v1","created_at":"2026-07-05T06:47:33.687942+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.01161","created_at":"2026-07-05T06:47:33.687942+00:00"},{"alias_kind":"pith_short_12","alias_value":"PEMQ3GCKBTJX","created_at":"2026-07-05T06:47:33.687942+00:00"},{"alias_kind":"pith_short_16","alias_value":"PEMQ3GCKBTJX6CBZ","created_at":"2026-07-05T06:47:33.687942+00:00"},{"alias_kind":"pith_short_8","alias_value":"PEMQ3GCK","created_at":"2026-07-05T06:47:33.687942+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/PEMQ3GCKBTJX6CBZPB3EQSKNAF","json":"https://pith.science/pith/PEMQ3GCKBTJX6CBZPB3EQSKNAF.json","graph_json":"https://pith.science/api/pith-number/PEMQ3GCKBTJX6CBZPB3EQSKNAF/graph.json","events_json":"https://pith.science/api/pith-number/PEMQ3GCKBTJX6CBZPB3EQSKNAF/events.json","paper":"https://pith.science/paper/PEMQ3GCK"},"agent_actions":{"view_html":"https://pith.science/pith/PEMQ3GCKBTJX6CBZPB3EQSKNAF","download_json":"https://pith.science/pith/PEMQ3GCKBTJX6CBZPB3EQSKNAF.json","view_paper":"https://pith.science/paper/PEMQ3GCK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.01161&json=true","fetch_graph":"https://pith.science/api/pith-number/PEMQ3GCKBTJX6CBZPB3EQSKNAF/graph.json","fetch_events":"https://pith.science/api/pith-number/PEMQ3GCKBTJX6CBZPB3EQSKNAF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PEMQ3GCKBTJX6CBZPB3EQSKNAF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PEMQ3GCKBTJX6CBZPB3EQSKNAF/action/storage_attestation","attest_author":"https://pith.science/pith/PEMQ3GCKBTJX6CBZPB3EQSKNAF/action/author_attestation","sign_citation":"https://pith.science/pith/PEMQ3GCKBTJX6CBZPB3EQSKNAF/action/citation_signature","submit_replication":"https://pith.science/pith/PEMQ3GCKBTJX6CBZPB3EQSKNAF/action/replication_record"}},"created_at":"2026-07-05T06:47:33.687942+00:00","updated_at":"2026-07-05T06:47:33.687942+00:00"}