{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:WQ7SGDSGXEBW53L4WVNSWO2G6S","short_pith_number":"pith:WQ7SGDSG","schema_version":"1.0","canonical_sha256":"b43f230e46b9036eed7cb55b2b3b46f48ad606383edd9f0bff29e5a02aac5603","source":{"kind":"arxiv","id":"2112.06305","version":1},"attestation_state":"computed","paper":{"title":"Recalibrating probabilistic forecasts of epidemics","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aaron Rumack, Roni Rosenfeld, Ryan J. Tibshirani","submitted_at":"2021-12-12T19:22:24Z","abstract_excerpt":"Distributional forecasts are important for a wide variety of applications, including forecasting epidemics. Often, forecasts are miscalibrated, or unreliable in assigning uncertainty to future events. We present a recalibration method that can be applied to a black-box forecaster given retrospective forecasts and observations, as well as an extension to make this method more effective in recalibrating epidemic forecasts. This method is guaranteed to improve calibration and log score performance when trained and measured in-sample. We also prove that the increase in expected log score of a reca"},"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":"2112.06305","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-12T19:22:24Z","cross_cats_sorted":[],"title_canon_sha256":"415102809f98aec24806cb800e7542b2f1970ff2b67a615aa5727a9068bf25fb","abstract_canon_sha256":"73889c7175a62d88b9ce6d515f667de3258cfc5a93618d9816ffd58c0f741b59"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:31:45.173951Z","signature_b64":"gU/PFII8Uk5NSrgEOlqGXu0BtosHSRijK+lzUxMyhY4tO8dW29fL17HcfEGC/SIvHC0OKoHn/71NZ2KBS6yCBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b43f230e46b9036eed7cb55b2b3b46f48ad606383edd9f0bff29e5a02aac5603","last_reissued_at":"2026-07-05T05:31:45.173510Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:31:45.173510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recalibrating probabilistic forecasts of epidemics","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aaron Rumack, Roni Rosenfeld, Ryan J. Tibshirani","submitted_at":"2021-12-12T19:22:24Z","abstract_excerpt":"Distributional forecasts are important for a wide variety of applications, including forecasting epidemics. Often, forecasts are miscalibrated, or unreliable in assigning uncertainty to future events. We present a recalibration method that can be applied to a black-box forecaster given retrospective forecasts and observations, as well as an extension to make this method more effective in recalibrating epidemic forecasts. This method is guaranteed to improve calibration and log score performance when trained and measured in-sample. We also prove that the increase in expected log score of a reca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.06305","kind":"arxiv","version":1},"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/2112.06305/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":"2112.06305","created_at":"2026-07-05T05:31:45.173571+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.06305v1","created_at":"2026-07-05T05:31:45.173571+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.06305","created_at":"2026-07-05T05:31:45.173571+00:00"},{"alias_kind":"pith_short_12","alias_value":"WQ7SGDSGXEBW","created_at":"2026-07-05T05:31:45.173571+00:00"},{"alias_kind":"pith_short_16","alias_value":"WQ7SGDSGXEBW53L4","created_at":"2026-07-05T05:31:45.173571+00:00"},{"alias_kind":"pith_short_8","alias_value":"WQ7SGDSG","created_at":"2026-07-05T05:31:45.173571+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/WQ7SGDSGXEBW53L4WVNSWO2G6S","json":"https://pith.science/pith/WQ7SGDSGXEBW53L4WVNSWO2G6S.json","graph_json":"https://pith.science/api/pith-number/WQ7SGDSGXEBW53L4WVNSWO2G6S/graph.json","events_json":"https://pith.science/api/pith-number/WQ7SGDSGXEBW53L4WVNSWO2G6S/events.json","paper":"https://pith.science/paper/WQ7SGDSG"},"agent_actions":{"view_html":"https://pith.science/pith/WQ7SGDSGXEBW53L4WVNSWO2G6S","download_json":"https://pith.science/pith/WQ7SGDSGXEBW53L4WVNSWO2G6S.json","view_paper":"https://pith.science/paper/WQ7SGDSG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.06305&json=true","fetch_graph":"https://pith.science/api/pith-number/WQ7SGDSGXEBW53L4WVNSWO2G6S/graph.json","fetch_events":"https://pith.science/api/pith-number/WQ7SGDSGXEBW53L4WVNSWO2G6S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WQ7SGDSGXEBW53L4WVNSWO2G6S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WQ7SGDSGXEBW53L4WVNSWO2G6S/action/storage_attestation","attest_author":"https://pith.science/pith/WQ7SGDSGXEBW53L4WVNSWO2G6S/action/author_attestation","sign_citation":"https://pith.science/pith/WQ7SGDSGXEBW53L4WVNSWO2G6S/action/citation_signature","submit_replication":"https://pith.science/pith/WQ7SGDSGXEBW53L4WVNSWO2G6S/action/replication_record"}},"created_at":"2026-07-05T05:31:45.173571+00:00","updated_at":"2026-07-05T05:31:45.173571+00:00"}