{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:44KLYU3LGY2CQTIMDZ25732G6V","short_pith_number":"pith:44KLYU3L","schema_version":"1.0","canonical_sha256":"e714bc536b3634284d0c1e75dfef46f54060d2c9b795024e5ee45a8ff1f00578","source":{"kind":"arxiv","id":"2407.11887","version":2},"attestation_state":"computed","paper":{"title":"On the optimal prediction of extreme events in heavy-tailed time series with applications to solar flare forecasting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Stilian Stoev, Victor Verma, Yang Chen","submitted_at":"2024-07-16T16:15:42Z","abstract_excerpt":"The prediction of extreme events in time series is a fundamental problem arising in many financial, scientific, engineering, and other applications. We begin by establishing a general Neyman-Pearson-type characterization of optimal extreme event predictors in terms of density ratios. This yields new insights and several closed-form optimal extreme event predictors for additive models. These results naturally extend to time series, where we study optimal extreme event prediction for both light- and heavy-tailed autoregressive and moving average models. Using a uniform law of large numbers for 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":"2407.11887","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"math.ST","submitted_at":"2024-07-16T16:15:42Z","cross_cats_sorted":["stat.AP","stat.ME","stat.TH"],"title_canon_sha256":"3a30664a464edcd63e74e917952412d8d0aec6ce9b883ea7dffb4b08a055d101","abstract_canon_sha256":"8b01b56d3411f7a6bc40323abe1258e64bb185ed30ba92fad639e2f4af19adee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:01:56.019828Z","signature_b64":"nYadVVGszn5mutVe57KI701f255gePd2kCci9ZF/THaIWWonW4dCesvyhWiD02e9NMuskbK0b8cOL/MH8Az+AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e714bc536b3634284d0c1e75dfef46f54060d2c9b795024e5ee45a8ff1f00578","last_reissued_at":"2026-07-05T10:01:56.019296Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:01:56.019296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the optimal prediction of extreme events in heavy-tailed time series with applications to solar flare forecasting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Stilian Stoev, Victor Verma, Yang Chen","submitted_at":"2024-07-16T16:15:42Z","abstract_excerpt":"The prediction of extreme events in time series is a fundamental problem arising in many financial, scientific, engineering, and other applications. We begin by establishing a general Neyman-Pearson-type characterization of optimal extreme event predictors in terms of density ratios. This yields new insights and several closed-form optimal extreme event predictors for additive models. These results naturally extend to time series, where we study optimal extreme event prediction for both light- and heavy-tailed autoregressive and moving average models. Using a uniform law of large numbers for e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.11887","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/2407.11887/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":"2407.11887","created_at":"2026-07-05T10:01:56.019359+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.11887v2","created_at":"2026-07-05T10:01:56.019359+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.11887","created_at":"2026-07-05T10:01:56.019359+00:00"},{"alias_kind":"pith_short_12","alias_value":"44KLYU3LGY2C","created_at":"2026-07-05T10:01:56.019359+00:00"},{"alias_kind":"pith_short_16","alias_value":"44KLYU3LGY2CQTIM","created_at":"2026-07-05T10:01:56.019359+00:00"},{"alias_kind":"pith_short_8","alias_value":"44KLYU3L","created_at":"2026-07-05T10:01:56.019359+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26270","citing_title":"On the optimal prediction of extreme events","ref_index":57,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V","json":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V.json","graph_json":"https://pith.science/api/pith-number/44KLYU3LGY2CQTIMDZ25732G6V/graph.json","events_json":"https://pith.science/api/pith-number/44KLYU3LGY2CQTIMDZ25732G6V/events.json","paper":"https://pith.science/paper/44KLYU3L"},"agent_actions":{"view_html":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V","download_json":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V.json","view_paper":"https://pith.science/paper/44KLYU3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.11887&json=true","fetch_graph":"https://pith.science/api/pith-number/44KLYU3LGY2CQTIMDZ25732G6V/graph.json","fetch_events":"https://pith.science/api/pith-number/44KLYU3LGY2CQTIMDZ25732G6V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V/action/storage_attestation","attest_author":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V/action/author_attestation","sign_citation":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V/action/citation_signature","submit_replication":"https://pith.science/pith/44KLYU3LGY2CQTIMDZ25732G6V/action/replication_record"}},"created_at":"2026-07-05T10:01:56.019359+00:00","updated_at":"2026-07-05T10:01:56.019359+00:00"}