{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PBECVSRSRVJOF2OY6J7SBRYC2F","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":"c4b27cba03484df116b19c3456b44b7bcca9bce916ae1e321f1e7c8474bbe9da","cross_cats_sorted":["math.PR","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-11T13:53:14Z","title_canon_sha256":"f9f69ec94ffc2f756a1577321277ad64f846cd3326549f70e083e3a3a04534ff"},"schema_version":"1.0","source":{"id":"2501.06547","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.06547","created_at":"2026-05-27T01:04:47Z"},{"alias_kind":"arxiv_version","alias_value":"2501.06547v4","created_at":"2026-05-27T01:04:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06547","created_at":"2026-05-27T01:04:47Z"},{"alias_kind":"pith_short_12","alias_value":"PBECVSRSRVJO","created_at":"2026-05-27T01:04:47Z"},{"alias_kind":"pith_short_16","alias_value":"PBECVSRSRVJOF2OY","created_at":"2026-05-27T01:04:47Z"},{"alias_kind":"pith_short_8","alias_value":"PBECVSRS","created_at":"2026-05-27T01:04:47Z"}],"graph_snapshots":[{"event_id":"sha256:2aed3f917c2d10dda51f1ff505b3580ffa66c7612e648bb8afcb663cf7e0462b","target":"graph","created_at":"2026-05-27T01:04:47Z","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/2501.06547/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The following learning problem arises naturally in various applications: Given a finite sample from a categorical or count time series, can we learn a function of the sample that (nearly) maximizes the probability of correctly guessing the values of a given portion of the data using the values from the remaining parts? Unlike classical approaches in statistical inference, our approach avoids explicitly estimating the conditional probabilities.\n  We propose a non-parametric guessing function with a learning rate independent of the alphabet size. Our analysis focuses on a broad class of time ser","authors_text":"D. Takahashi, J.-R. Chazottes, S. Gallo","cross_cats":["math.PR","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-11T13:53:14Z","title":"Pathwise guessing in categorical time series with unbounded alphabets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06547","kind":"arxiv","version":4},"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:abe7ff277db349fe1ac3a983fa960d39a25fe6fff31e53f356070f913bc2e380","target":"record","created_at":"2026-05-27T01:04:47Z","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":"c4b27cba03484df116b19c3456b44b7bcca9bce916ae1e321f1e7c8474bbe9da","cross_cats_sorted":["math.PR","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-11T13:53:14Z","title_canon_sha256":"f9f69ec94ffc2f756a1577321277ad64f846cd3326549f70e083e3a3a04534ff"},"schema_version":"1.0","source":{"id":"2501.06547","kind":"arxiv","version":4}},"canonical_sha256":"78482aca328d52e2e9d8f27f20c702d16b535cf3b5f732a468a59f3c07f7717f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78482aca328d52e2e9d8f27f20c702d16b535cf3b5f732a468a59f3c07f7717f","first_computed_at":"2026-05-27T01:04:47.670310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-27T01:04:47.670310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CxQabilEJBDYFHlCqPoJveujoN66Mmk4jLIB34+D5tX1gGIMM5vv/0KwHBZk5L4T0zCs4lCL9cBtekvasvvXAg==","signature_status":"signed_v1","signed_at":"2026-05-27T01:04:47.670941Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.06547","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:abe7ff277db349fe1ac3a983fa960d39a25fe6fff31e53f356070f913bc2e380","sha256:2aed3f917c2d10dda51f1ff505b3580ffa66c7612e648bb8afcb663cf7e0462b"],"state_sha256":"ba498d83ed07bd842ff1400dae1130aa30fc3abbc1e57a1ba40b710c7b8547a3"}