{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PBECVSRSRVJOF2OY6J7SBRYC2F","short_pith_number":"pith:PBECVSRS","schema_version":"1.0","canonical_sha256":"78482aca328d52e2e9d8f27f20c702d16b535cf3b5f732a468a59f3c07f7717f","source":{"kind":"arxiv","id":"2501.06547","version":4},"attestation_state":"computed","paper":{"title":"Pathwise guessing in categorical time series with unbounded alphabets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.PR","stat.TH"],"primary_cat":"math.ST","authors_text":"D. Takahashi, J.-R. Chazottes, S. Gallo","submitted_at":"2025-01-11T13:53:14Z","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"},"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":"2501.06547","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-11T13:53:14Z","cross_cats_sorted":["math.PR","stat.TH"],"title_canon_sha256":"f9f69ec94ffc2f756a1577321277ad64f846cd3326549f70e083e3a3a04534ff","abstract_canon_sha256":"c4b27cba03484df116b19c3456b44b7bcca9bce916ae1e321f1e7c8474bbe9da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:04:47.670941Z","signature_b64":"CxQabilEJBDYFHlCqPoJveujoN66Mmk4jLIB34+D5tX1gGIMM5vv/0KwHBZk5L4T0zCs4lCL9cBtekvasvvXAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78482aca328d52e2e9d8f27f20c702d16b535cf3b5f732a468a59f3c07f7717f","last_reissued_at":"2026-05-27T01:04:47.670310Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:04:47.670310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pathwise guessing in categorical time series with unbounded alphabets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.PR","stat.TH"],"primary_cat":"math.ST","authors_text":"D. Takahashi, J.-R. Chazottes, S. Gallo","submitted_at":"2025-01-11T13:53:14Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06547","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.06547/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2501.06547","created_at":"2026-05-27T01:04:47.670416+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.06547v4","created_at":"2026-05-27T01:04:47.670416+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06547","created_at":"2026-05-27T01:04:47.670416+00:00"},{"alias_kind":"pith_short_12","alias_value":"PBECVSRSRVJO","created_at":"2026-05-27T01:04:47.670416+00:00"},{"alias_kind":"pith_short_16","alias_value":"PBECVSRSRVJOF2OY","created_at":"2026-05-27T01:04:47.670416+00:00"},{"alias_kind":"pith_short_8","alias_value":"PBECVSRS","created_at":"2026-05-27T01:04:47.670416+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/PBECVSRSRVJOF2OY6J7SBRYC2F","json":"https://pith.science/pith/PBECVSRSRVJOF2OY6J7SBRYC2F.json","graph_json":"https://pith.science/api/pith-number/PBECVSRSRVJOF2OY6J7SBRYC2F/graph.json","events_json":"https://pith.science/api/pith-number/PBECVSRSRVJOF2OY6J7SBRYC2F/events.json","paper":"https://pith.science/paper/PBECVSRS"},"agent_actions":{"view_html":"https://pith.science/pith/PBECVSRSRVJOF2OY6J7SBRYC2F","download_json":"https://pith.science/pith/PBECVSRSRVJOF2OY6J7SBRYC2F.json","view_paper":"https://pith.science/paper/PBECVSRS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.06547&json=true","fetch_graph":"https://pith.science/api/pith-number/PBECVSRSRVJOF2OY6J7SBRYC2F/graph.json","fetch_events":"https://pith.science/api/pith-number/PBECVSRSRVJOF2OY6J7SBRYC2F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PBECVSRSRVJOF2OY6J7SBRYC2F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PBECVSRSRVJOF2OY6J7SBRYC2F/action/storage_attestation","attest_author":"https://pith.science/pith/PBECVSRSRVJOF2OY6J7SBRYC2F/action/author_attestation","sign_citation":"https://pith.science/pith/PBECVSRSRVJOF2OY6J7SBRYC2F/action/citation_signature","submit_replication":"https://pith.science/pith/PBECVSRSRVJOF2OY6J7SBRYC2F/action/replication_record"}},"created_at":"2026-05-27T01:04:47.670416+00:00","updated_at":"2026-05-27T01:04:47.670416+00:00"}