{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:BLS4BD7DOFATN4DJ5UYGC7SALM","short_pith_number":"pith:BLS4BD7D","schema_version":"1.0","canonical_sha256":"0ae5c08fe3714136f069ed30617e405b1dfeac8c07d79033a6b92fe972163023","source":{"kind":"arxiv","id":"1308.0642","version":4},"attestation_state":"computed","paper":{"title":"Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Emanuel Parzen, Subhadeep Mukhopadhyay","submitted_at":"2013-08-03T00:04:00Z","abstract_excerpt":"A new comprehensive approach to nonlinear time series analysis and modeling is developed in the present paper. We introduce novel data-specific mid-distribution based Legendre Polynomial (LP) like nonlinear transformations of the original time series Y(t) that enables us to adapt all the existing stationary linear Gaussian time series modeling strategy and made it applicable for non-Gaussian and nonlinear processes in a robust fashion. The emphasis of the present paper is on empirical time series modeling via the algorithm LPTime. We demonstrate the effectiveness of our theoretical framework u"},"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":"1308.0642","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-08-03T00:04:00Z","cross_cats_sorted":["stat.AP","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"2910c60cbecffd8f4c98a17d4b9ff6122c718a6bba533aa23eae191bb18688e2","abstract_canon_sha256":"5df92f8e6c709c1f2ea32be57f181c7bd0df59de708adf9fe155a263a389ae5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:22.788764Z","signature_b64":"eCuxhYXqNhkqu99tQQzDK/KpkTMSEnSDXkvVeoo40I++AVaw+czIplNfCy666ah77qEvsvYE34XS74y8MNitAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ae5c08fe3714136f069ed30617e405b1dfeac8c07d79033a6b92fe972163023","last_reissued_at":"2026-05-18T00:27:22.788070Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:22.788070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Emanuel Parzen, Subhadeep Mukhopadhyay","submitted_at":"2013-08-03T00:04:00Z","abstract_excerpt":"A new comprehensive approach to nonlinear time series analysis and modeling is developed in the present paper. We introduce novel data-specific mid-distribution based Legendre Polynomial (LP) like nonlinear transformations of the original time series Y(t) that enables us to adapt all the existing stationary linear Gaussian time series modeling strategy and made it applicable for non-Gaussian and nonlinear processes in a robust fashion. The emphasis of the present paper is on empirical time series modeling via the algorithm LPTime. We demonstrate the effectiveness of our theoretical framework u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1308.0642","kind":"arxiv","version":4},"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":"1308.0642","created_at":"2026-05-18T00:27:22.788182+00:00"},{"alias_kind":"arxiv_version","alias_value":"1308.0642v4","created_at":"2026-05-18T00:27:22.788182+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1308.0642","created_at":"2026-05-18T00:27:22.788182+00:00"},{"alias_kind":"pith_short_12","alias_value":"BLS4BD7DOFAT","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_16","alias_value":"BLS4BD7DOFATN4DJ","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_8","alias_value":"BLS4BD7D","created_at":"2026-05-18T12:27:40.988391+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/BLS4BD7DOFATN4DJ5UYGC7SALM","json":"https://pith.science/pith/BLS4BD7DOFATN4DJ5UYGC7SALM.json","graph_json":"https://pith.science/api/pith-number/BLS4BD7DOFATN4DJ5UYGC7SALM/graph.json","events_json":"https://pith.science/api/pith-number/BLS4BD7DOFATN4DJ5UYGC7SALM/events.json","paper":"https://pith.science/paper/BLS4BD7D"},"agent_actions":{"view_html":"https://pith.science/pith/BLS4BD7DOFATN4DJ5UYGC7SALM","download_json":"https://pith.science/pith/BLS4BD7DOFATN4DJ5UYGC7SALM.json","view_paper":"https://pith.science/paper/BLS4BD7D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1308.0642&json=true","fetch_graph":"https://pith.science/api/pith-number/BLS4BD7DOFATN4DJ5UYGC7SALM/graph.json","fetch_events":"https://pith.science/api/pith-number/BLS4BD7DOFATN4DJ5UYGC7SALM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BLS4BD7DOFATN4DJ5UYGC7SALM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BLS4BD7DOFATN4DJ5UYGC7SALM/action/storage_attestation","attest_author":"https://pith.science/pith/BLS4BD7DOFATN4DJ5UYGC7SALM/action/author_attestation","sign_citation":"https://pith.science/pith/BLS4BD7DOFATN4DJ5UYGC7SALM/action/citation_signature","submit_replication":"https://pith.science/pith/BLS4BD7DOFATN4DJ5UYGC7SALM/action/replication_record"}},"created_at":"2026-05-18T00:27:22.788182+00:00","updated_at":"2026-05-18T00:27:22.788182+00:00"}