{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ABOG5RNWVQRNR4FWUMORF7OJWL","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":"3ddb3e7a1d4cb11919db57ea99912a184e1ea2ca080650e22ab0ca97320b9e30","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-06T14:52:56Z","title_canon_sha256":"7eccd4ab7459a28d81b783ff131256344ce4c42a87c9dfe8ed3112c245875ecd"},"schema_version":"1.0","source":{"id":"2302.02834","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.02834","created_at":"2026-07-05T09:05:08Z"},{"alias_kind":"arxiv_version","alias_value":"2302.02834v2","created_at":"2026-07-05T09:05:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.02834","created_at":"2026-07-05T09:05:08Z"},{"alias_kind":"pith_short_12","alias_value":"ABOG5RNWVQRN","created_at":"2026-07-05T09:05:08Z"},{"alias_kind":"pith_short_16","alias_value":"ABOG5RNWVQRNR4FW","created_at":"2026-07-05T09:05:08Z"},{"alias_kind":"pith_short_8","alias_value":"ABOG5RNW","created_at":"2026-07-05T09:05:08Z"}],"graph_snapshots":[{"event_id":"sha256:3f7185ea5b6e025312b8427be78c3295d7139edaaf06de38f51ca26127ce3631","target":"graph","created_at":"2026-07-05T09:05:08Z","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/2302.02834/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning models play a vital role in time series forecasting. These models, however, often overlook an important element: point uncertainty estimates. Incorporating these estimates is crucial for effective risk management, informed model selection, and decision-making.To address this issue, our research introduces a method for uncertainty estimation. We employ a surrogate Gaussian process regression model. It enhances any base regression model with reasonable uncertainty estimates. This approach stands out for its computational efficiency. It only necessitates training one supplementar","authors_text":"Alexey Zaytsev, Evgeny Sokolovskiy, Leonid Erlygin, Valeriia Baklanova, Vladimir Zholobov","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-06T14:52:56Z","title":"Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.02834","kind":"arxiv","version":2},"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:ccc268512a9f9b034ae520469fe6ef9c14a6cd70729c77da97c4048b861b50a4","target":"record","created_at":"2026-07-05T09:05:08Z","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":"3ddb3e7a1d4cb11919db57ea99912a184e1ea2ca080650e22ab0ca97320b9e30","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-06T14:52:56Z","title_canon_sha256":"7eccd4ab7459a28d81b783ff131256344ce4c42a87c9dfe8ed3112c245875ecd"},"schema_version":"1.0","source":{"id":"2302.02834","kind":"arxiv","version":2}},"canonical_sha256":"005c6ec5b6ac22d8f0b6a31d12fdc9b2c1e1cbd166ab8331d3c109a5de5cf8fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"005c6ec5b6ac22d8f0b6a31d12fdc9b2c1e1cbd166ab8331d3c109a5de5cf8fb","first_computed_at":"2026-07-05T09:05:08.953826Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:05:08.953826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GqXBLJXjxL8lz/JNaDsVrqY1N0XcJSA/RzmcBY/ckoai7kjdRxP5PqyFYVMcja4xNwkNQvijtubpaNSPZr9oBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:05:08.954235Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.02834","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ccc268512a9f9b034ae520469fe6ef9c14a6cd70729c77da97c4048b861b50a4","sha256:3f7185ea5b6e025312b8427be78c3295d7139edaaf06de38f51ca26127ce3631"],"state_sha256":"e58a92dff254cd7db3231615637c72965c53e4a22bc15f7279bb4875b2a71d72"}