{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:CEC2Q75OXHRW3TAR3GQUOSSMGZ","short_pith_number":"pith:CEC2Q75O","schema_version":"1.0","canonical_sha256":"1105a87faeb9e36dcc11d9a1474a4c36568820829090933dcce362ea6deff81d","source":{"kind":"arxiv","id":"1312.6969","version":1},"attestation_state":"computed","paper":{"title":"Time series modeling by a regression approach based on a latent process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Allou Sam\\'e, Faicel Chamroukhi, G\\'erard Govaert, Patrice Aknin","submitted_at":"2013-12-25T13:13:55Z","abstract_excerpt":"Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization ("},"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":"1312.6969","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-12-25T13:13:55Z","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"feed9f9e2f3fb34a2787f1daf5822c82cffac231a4c657ec5ed3ad347e45857f","abstract_canon_sha256":"6ce0256c1c6a75f240f80d873e8a9ff911cccb9a6a068b1fa7125eb8c7e837c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:03:46.273838Z","signature_b64":"5BgUVueiul0/r5ynvPdhCzEVXY21CT4OofJQ+GBQ5zl0D5114KDnO9AvGwNekuFtpD2T9AU6mDLKgfLN6ANVAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1105a87faeb9e36dcc11d9a1474a4c36568820829090933dcce362ea6deff81d","last_reissued_at":"2026-05-18T03:03:46.272976Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:03:46.272976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time series modeling by a regression approach based on a latent process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Allou Sam\\'e, Faicel Chamroukhi, G\\'erard Govaert, Patrice Aknin","submitted_at":"2013-12-25T13:13:55Z","abstract_excerpt":"Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.6969","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":"1312.6969","created_at":"2026-05-18T03:03:46.273125+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.6969v1","created_at":"2026-05-18T03:03:46.273125+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.6969","created_at":"2026-05-18T03:03:46.273125+00:00"},{"alias_kind":"pith_short_12","alias_value":"CEC2Q75OXHRW","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_16","alias_value":"CEC2Q75OXHRW3TAR","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_8","alias_value":"CEC2Q75O","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/CEC2Q75OXHRW3TAR3GQUOSSMGZ","json":"https://pith.science/pith/CEC2Q75OXHRW3TAR3GQUOSSMGZ.json","graph_json":"https://pith.science/api/pith-number/CEC2Q75OXHRW3TAR3GQUOSSMGZ/graph.json","events_json":"https://pith.science/api/pith-number/CEC2Q75OXHRW3TAR3GQUOSSMGZ/events.json","paper":"https://pith.science/paper/CEC2Q75O"},"agent_actions":{"view_html":"https://pith.science/pith/CEC2Q75OXHRW3TAR3GQUOSSMGZ","download_json":"https://pith.science/pith/CEC2Q75OXHRW3TAR3GQUOSSMGZ.json","view_paper":"https://pith.science/paper/CEC2Q75O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.6969&json=true","fetch_graph":"https://pith.science/api/pith-number/CEC2Q75OXHRW3TAR3GQUOSSMGZ/graph.json","fetch_events":"https://pith.science/api/pith-number/CEC2Q75OXHRW3TAR3GQUOSSMGZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CEC2Q75OXHRW3TAR3GQUOSSMGZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CEC2Q75OXHRW3TAR3GQUOSSMGZ/action/storage_attestation","attest_author":"https://pith.science/pith/CEC2Q75OXHRW3TAR3GQUOSSMGZ/action/author_attestation","sign_citation":"https://pith.science/pith/CEC2Q75OXHRW3TAR3GQUOSSMGZ/action/citation_signature","submit_replication":"https://pith.science/pith/CEC2Q75OXHRW3TAR3GQUOSSMGZ/action/replication_record"}},"created_at":"2026-05-18T03:03:46.273125+00:00","updated_at":"2026-05-18T03:03:46.273125+00:00"}