{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:YXNXPD5XR4PIRYY67R4SBOF5DF","short_pith_number":"pith:YXNXPD5X","schema_version":"1.0","canonical_sha256":"c5db778fb78f1e88e31efc7920b8bd195a3bdd0f41953fa5e04565eaa680899b","source":{"kind":"arxiv","id":"1409.1842","version":1},"attestation_state":"computed","paper":{"title":"On Optimal Multiple Changepoint Algorithms for Large Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Guillem Rigaill, Paul Fearnhead, Robert Maidstone, Toby Hocking","submitted_at":"2014-09-05T15:44:34Z","abstract_excerpt":"There is an increasing need for algorithms that can accurately detect changepoints in long time-series, or equivalent, data. Many common approaches to detecting changepoints, for example based on penalised likelihood or minimum description length, can be formulated in terms of minimising a cost over segmentations. Dynamic programming methods exist to solve this minimisation problem exactly, but these tend to scale at least quadratically in the length of the time-series. Algorithms, such as Binary Segmentation, exist that have a computational cost that is close to linear in the length of the ti"},"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":"1409.1842","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-09-05T15:44:34Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"4055279e6d91c9deb6f5f854fb487277bb43160b772bb9fcfc4e5990a4c421c5","abstract_canon_sha256":"6e11cf169c65a6d498ab22d2627c15b75f7b2de6a173203b571bdeda9e90dc50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:25.088183Z","signature_b64":"ppoDPv9RJ3MRoGvax8HJnzcW2ruVkwx+NxYADURCqvkoJC8kW1OXotqnBnPQdAQxsR9vecZzrl+EHyODgWosCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5db778fb78f1e88e31efc7920b8bd195a3bdd0f41953fa5e04565eaa680899b","last_reissued_at":"2026-05-18T02:43:25.087436Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:25.087436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Optimal Multiple Changepoint Algorithms for Large Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Guillem Rigaill, Paul Fearnhead, Robert Maidstone, Toby Hocking","submitted_at":"2014-09-05T15:44:34Z","abstract_excerpt":"There is an increasing need for algorithms that can accurately detect changepoints in long time-series, or equivalent, data. Many common approaches to detecting changepoints, for example based on penalised likelihood or minimum description length, can be formulated in terms of minimising a cost over segmentations. Dynamic programming methods exist to solve this minimisation problem exactly, but these tend to scale at least quadratically in the length of the time-series. Algorithms, such as Binary Segmentation, exist that have a computational cost that is close to linear in the length of the ti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.1842","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":"1409.1842","created_at":"2026-05-18T02:43:25.087554+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.1842v1","created_at":"2026-05-18T02:43:25.087554+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.1842","created_at":"2026-05-18T02:43:25.087554+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXNXPD5XR4PI","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXNXPD5XR4PIRYY6","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXNXPD5X","created_at":"2026-05-18T12:28:59.999130+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/YXNXPD5XR4PIRYY67R4SBOF5DF","json":"https://pith.science/pith/YXNXPD5XR4PIRYY67R4SBOF5DF.json","graph_json":"https://pith.science/api/pith-number/YXNXPD5XR4PIRYY67R4SBOF5DF/graph.json","events_json":"https://pith.science/api/pith-number/YXNXPD5XR4PIRYY67R4SBOF5DF/events.json","paper":"https://pith.science/paper/YXNXPD5X"},"agent_actions":{"view_html":"https://pith.science/pith/YXNXPD5XR4PIRYY67R4SBOF5DF","download_json":"https://pith.science/pith/YXNXPD5XR4PIRYY67R4SBOF5DF.json","view_paper":"https://pith.science/paper/YXNXPD5X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.1842&json=true","fetch_graph":"https://pith.science/api/pith-number/YXNXPD5XR4PIRYY67R4SBOF5DF/graph.json","fetch_events":"https://pith.science/api/pith-number/YXNXPD5XR4PIRYY67R4SBOF5DF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXNXPD5XR4PIRYY67R4SBOF5DF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXNXPD5XR4PIRYY67R4SBOF5DF/action/storage_attestation","attest_author":"https://pith.science/pith/YXNXPD5XR4PIRYY67R4SBOF5DF/action/author_attestation","sign_citation":"https://pith.science/pith/YXNXPD5XR4PIRYY67R4SBOF5DF/action/citation_signature","submit_replication":"https://pith.science/pith/YXNXPD5XR4PIRYY67R4SBOF5DF/action/replication_record"}},"created_at":"2026-05-18T02:43:25.087554+00:00","updated_at":"2026-05-18T02:43:25.087554+00:00"}