{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BJSTMQ2NDQVIYWTF4QUJRP5UX6","short_pith_number":"pith:BJSTMQ2N","schema_version":"1.0","canonical_sha256":"0a6536434d1c2a8c5a65e42898bfb4bf97808c32d3ca7620982db807a676b6d3","source":{"kind":"arxiv","id":"2502.00818","version":2},"attestation_state":"computed","paper":{"title":"Error-quantified Conformal Inference for Time Series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Changliang Zou, Dongjian Hu, Junxi Wu, Shu-Tao Xia, Yajie Bao","submitted_at":"2025-02-02T15:02:36Z","abstract_excerpt":"Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. Conformal inference provides a pivotal and flexible instrument for assessing the uncertainty of machine learning models through prediction sets. Recently, a series of online conformal inference methods updated thresholds of prediction sets by performing online gradient descent on a sequence of quantile loss functions. A drawback of such methods is that they only use the information of revealed non-conformity scores via miscoverage indicators but ignore e"},"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":"2502.00818","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-02-02T15:02:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c4752e2b72f06e82349d74ed7f8ba25ed1ddc516e93b41b42fc7715dac60db86","abstract_canon_sha256":"b252a687774b76040557f83e244cc95e4d2164402a6b1f3b9194388b841e0369"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:05.350581Z","signature_b64":"g/6t09lr5hiuL6mlb+RYmfZHLbOIhiv8hkMuxXL8u7jeNvRQvn1Wj5agAYIHmk+8DLg4NO7Tchdh1PXorp+NDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a6536434d1c2a8c5a65e42898bfb4bf97808c32d3ca7620982db807a676b6d3","last_reissued_at":"2026-07-05T12:06:05.350026Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:05.350026Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Error-quantified Conformal Inference for Time Series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Changliang Zou, Dongjian Hu, Junxi Wu, Shu-Tao Xia, Yajie Bao","submitted_at":"2025-02-02T15:02:36Z","abstract_excerpt":"Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. Conformal inference provides a pivotal and flexible instrument for assessing the uncertainty of machine learning models through prediction sets. Recently, a series of online conformal inference methods updated thresholds of prediction sets by performing online gradient descent on a sequence of quantile loss functions. A drawback of such methods is that they only use the information of revealed non-conformity scores via miscoverage indicators but ignore e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00818","kind":"arxiv","version":2},"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/2502.00818/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":"2502.00818","created_at":"2026-07-05T12:06:05.350094+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.00818v2","created_at":"2026-07-05T12:06:05.350094+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00818","created_at":"2026-07-05T12:06:05.350094+00:00"},{"alias_kind":"pith_short_12","alias_value":"BJSTMQ2NDQVI","created_at":"2026-07-05T12:06:05.350094+00:00"},{"alias_kind":"pith_short_16","alias_value":"BJSTMQ2NDQVIYWTF","created_at":"2026-07-05T12:06:05.350094+00:00"},{"alias_kind":"pith_short_8","alias_value":"BJSTMQ2N","created_at":"2026-07-05T12:06:05.350094+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.04406","citing_title":"Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6","json":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6.json","graph_json":"https://pith.science/api/pith-number/BJSTMQ2NDQVIYWTF4QUJRP5UX6/graph.json","events_json":"https://pith.science/api/pith-number/BJSTMQ2NDQVIYWTF4QUJRP5UX6/events.json","paper":"https://pith.science/paper/BJSTMQ2N"},"agent_actions":{"view_html":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6","download_json":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6.json","view_paper":"https://pith.science/paper/BJSTMQ2N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.00818&json=true","fetch_graph":"https://pith.science/api/pith-number/BJSTMQ2NDQVIYWTF4QUJRP5UX6/graph.json","fetch_events":"https://pith.science/api/pith-number/BJSTMQ2NDQVIYWTF4QUJRP5UX6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6/action/storage_attestation","attest_author":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6/action/author_attestation","sign_citation":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6/action/citation_signature","submit_replication":"https://pith.science/pith/BJSTMQ2NDQVIYWTF4QUJRP5UX6/action/replication_record"}},"created_at":"2026-07-05T12:06:05.350094+00:00","updated_at":"2026-07-05T12:06:05.350094+00:00"}