{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:YWGUN3VFBEPUUNJ4FMN52XC7LN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4f8ff6dedf1886064ff91fef5e722360cc114cc71a1b95213091a8acafd0cf2a","cross_cats_sorted":["cs.AI","stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-27T08:04:53Z","title_canon_sha256":"0ac29e0338db3916e6ec4438be357ec0f7496b295d968ffffe956a2e8656a4e5"},"schema_version":"1.0","source":{"id":"2006.15311","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.15311","created_at":"2026-07-05T01:14:04Z"},{"alias_kind":"arxiv_version","alias_value":"2006.15311v1","created_at":"2026-07-05T01:14:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.15311","created_at":"2026-07-05T01:14:04Z"},{"alias_kind":"pith_short_12","alias_value":"YWGUN3VFBEPU","created_at":"2026-07-05T01:14:04Z"},{"alias_kind":"pith_short_16","alias_value":"YWGUN3VFBEPUUNJ4","created_at":"2026-07-05T01:14:04Z"},{"alias_kind":"pith_short_8","alias_value":"YWGUN3VF","created_at":"2026-07-05T01:14:04Z"}],"graph_snapshots":[{"event_id":"sha256:678688b5f32824e01428ff4b264bf5ce28bf3d0a864513f050071fc683e088e5","target":"graph","created_at":"2026-07-05T01:14:04Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2006.15311/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stream classification methods classify a continuous stream of data as new labelled samples arrive. They often also have to deal with concept drift. This paper focuses on seasonal drift in stream classification, which can be found in many real-world application data sources. Traditional approaches of stream classification consider seasonal drift by including seasonal dummy/indicator variables or building separate models for each season. But these approaches have strong limitations in high-dimensional classification problems, or with complex seasonal patterns. This paper explores how to best han","authors_text":"Christoph Bergmeir, Francois Petitjean, Rakshitha Godahewa, Trevor Yann","cross_cats":["cs.AI","stat.AP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-27T08:04:53Z","title":"Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.15311","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a352a8f2774b4a45443001c4b2664a1f4219b32691fcf033c8f5abeb6fc33031","target":"record","created_at":"2026-07-05T01:14:04Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4f8ff6dedf1886064ff91fef5e722360cc114cc71a1b95213091a8acafd0cf2a","cross_cats_sorted":["cs.AI","stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-27T08:04:53Z","title_canon_sha256":"0ac29e0338db3916e6ec4438be357ec0f7496b295d968ffffe956a2e8656a4e5"},"schema_version":"1.0","source":{"id":"2006.15311","kind":"arxiv","version":1}},"canonical_sha256":"c58d46eea5091f4a353c2b1bdd5c5f5b4c3a0d282843742bc46d20e4b0edc802","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c58d46eea5091f4a353c2b1bdd5c5f5b4c3a0d282843742bc46d20e4b0edc802","first_computed_at":"2026-07-05T01:14:04.441892Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:14:04.441892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ayyfMFSxOppSbE+M/pEWkyYNomihKA/eK8cDvT0OEi8UAMUkhZH5ilMzJCiPQgnygyy93zRIT6kvB5JkI6quAw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:14:04.442306Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.15311","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a352a8f2774b4a45443001c4b2664a1f4219b32691fcf033c8f5abeb6fc33031","sha256:678688b5f32824e01428ff4b264bf5ce28bf3d0a864513f050071fc683e088e5"],"state_sha256":"8345302c099636b811444e7ed9abf9fdce444294d06f11da66c53549918878c7"}