{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:AUIHEW7LFL6ZJ3RZOCAY4LNO2Z","short_pith_number":"pith:AUIHEW7L","schema_version":"1.0","canonical_sha256":"0510725beb2afd94ee3970818e2daed64532f3ec7fc5d9e068e86a118553e460","source":{"kind":"arxiv","id":"1510.04850","version":3},"attestation_state":"computed","paper":{"title":"Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Cesare Alippi, Diego Carrera, Giacomo Boracchi, Manuel Roveri","submitted_at":"2015-10-16T11:54:05Z","abstract_excerpt":"We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data dimension scales. In particular, we consider a general approach where changes are detected by comparing the distribution of the log-likelihood of the datastream over different time windows. Despite the fact that this approach constitutes the frame of several change-detection methods, its effectiveness when data dimension scales has never been investigated, which is indeed the goal of our paper. We show that the magnitude o"},"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":"1510.04850","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-16T11:54:05Z","cross_cats_sorted":[],"title_canon_sha256":"192e4864479bea04116c28bdddef0e360557035fce7a0d45a9de0f3ae886a522","abstract_canon_sha256":"fad00cdfc5ac188543015350198b371efc7e7880d338cfe623414907e06537c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:11.064694Z","signature_b64":"puOIDRU9aVQ3NT2NQ4B8m4XlDzGTr/SSojDNxyxkq5qEQ1Ez27lRyxsK7Naz1ESBtL+VYzVN0wXLWHyWGr3pCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0510725beb2afd94ee3970818e2daed64532f3ec7fc5d9e068e86a118553e460","last_reissued_at":"2026-05-18T00:29:11.064013Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:11.064013Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Cesare Alippi, Diego Carrera, Giacomo Boracchi, Manuel Roveri","submitted_at":"2015-10-16T11:54:05Z","abstract_excerpt":"We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data dimension scales. In particular, we consider a general approach where changes are detected by comparing the distribution of the log-likelihood of the datastream over different time windows. Despite the fact that this approach constitutes the frame of several change-detection methods, its effectiveness when data dimension scales has never been investigated, which is indeed the goal of our paper. We show that the magnitude o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.04850","kind":"arxiv","version":3},"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":"1510.04850","created_at":"2026-05-18T00:29:11.064096+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.04850v3","created_at":"2026-05-18T00:29:11.064096+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.04850","created_at":"2026-05-18T00:29:11.064096+00:00"},{"alias_kind":"pith_short_12","alias_value":"AUIHEW7LFL6Z","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_16","alias_value":"AUIHEW7LFL6ZJ3RZ","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_8","alias_value":"AUIHEW7L","created_at":"2026-05-18T12:29:10.953037+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/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z","json":"https://pith.science/pith/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z.json","graph_json":"https://pith.science/api/pith-number/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/graph.json","events_json":"https://pith.science/api/pith-number/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/events.json","paper":"https://pith.science/paper/AUIHEW7L"},"agent_actions":{"view_html":"https://pith.science/pith/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z","download_json":"https://pith.science/pith/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z.json","view_paper":"https://pith.science/paper/AUIHEW7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.04850&json=true","fetch_graph":"https://pith.science/api/pith-number/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/graph.json","fetch_events":"https://pith.science/api/pith-number/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/action/storage_attestation","attest_author":"https://pith.science/pith/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/action/author_attestation","sign_citation":"https://pith.science/pith/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/action/citation_signature","submit_replication":"https://pith.science/pith/AUIHEW7LFL6ZJ3RZOCAY4LNO2Z/action/replication_record"}},"created_at":"2026-05-18T00:29:11.064096+00:00","updated_at":"2026-05-18T00:29:11.064096+00:00"}