{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:V3AT2AKCXXXDPSRGL3RO73ISBS","short_pith_number":"pith:V3AT2AKC","schema_version":"1.0","canonical_sha256":"aec13d0142bdee37ca265ee2efed120cad2ae305e479f215a719d2b1765b26f4","source":{"kind":"arxiv","id":"2302.00411","version":3},"attestation_state":"computed","paper":{"title":"Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.CP"],"primary_cat":"stat.AP","authors_text":"Bartosz Uniejewski","submitted_at":"2023-02-01T12:52:20Z","abstract_excerpt":"Accurate short-term price forecasting is essential for daily operations in electricity markets. This article introduces a new method, called Smoothing Quantile Regression (SQR) Averaging, that improves upon well-performing probabilistic forecasting schemes. To demonstrate its utility, a comprehensive study is conducted on two electricity markets, including recent data covering the COVID-19 pandemic and the Russian invasion of Ukraine. The performance of SQR Averaging is evaluated both in terms of reliability and sharpness measures, and economic benefits from a trading strategy. The latter util"},"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":"2302.00411","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2023-02-01T12:52:20Z","cross_cats_sorted":["q-fin.CP"],"title_canon_sha256":"1e3d549c7ebf0cf20cdb7f0ed34658f98fdfc979abc8f75e1d9d69b229e7d9ff","abstract_canon_sha256":"d3894471800b3e287218c37f950881caab996ab5a5f6f5c3b06e27e014eb34a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:20.710307Z","signature_b64":"Sh6zD5ly8k+hhxVqwQL28gRdlYzCA0cxgYDB4qWxiGFs0SZ/L6Rmtgk6BpT+sD9qporIBTbfDJ/fwG5U1W5zCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aec13d0142bdee37ca265ee2efed120cad2ae305e479f215a719d2b1765b26f4","last_reissued_at":"2026-07-05T12:03:20.709684Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:20.709684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.CP"],"primary_cat":"stat.AP","authors_text":"Bartosz Uniejewski","submitted_at":"2023-02-01T12:52:20Z","abstract_excerpt":"Accurate short-term price forecasting is essential for daily operations in electricity markets. This article introduces a new method, called Smoothing Quantile Regression (SQR) Averaging, that improves upon well-performing probabilistic forecasting schemes. To demonstrate its utility, a comprehensive study is conducted on two electricity markets, including recent data covering the COVID-19 pandemic and the Russian invasion of Ukraine. The performance of SQR Averaging is evaluated both in terms of reliability and sharpness measures, and economic benefits from a trading strategy. The latter util"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.00411","kind":"arxiv","version":3},"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/2302.00411/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":"2302.00411","created_at":"2026-07-05T12:03:20.709768+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.00411v3","created_at":"2026-07-05T12:03:20.709768+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.00411","created_at":"2026-07-05T12:03:20.709768+00:00"},{"alias_kind":"pith_short_12","alias_value":"V3AT2AKCXXXD","created_at":"2026-07-05T12:03:20.709768+00:00"},{"alias_kind":"pith_short_16","alias_value":"V3AT2AKCXXXDPSRG","created_at":"2026-07-05T12:03:20.709768+00:00"},{"alias_kind":"pith_short_8","alias_value":"V3AT2AKC","created_at":"2026-07-05T12:03:20.709768+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.04935","citing_title":"Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market","ref_index":1255,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS","json":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS.json","graph_json":"https://pith.science/api/pith-number/V3AT2AKCXXXDPSRGL3RO73ISBS/graph.json","events_json":"https://pith.science/api/pith-number/V3AT2AKCXXXDPSRGL3RO73ISBS/events.json","paper":"https://pith.science/paper/V3AT2AKC"},"agent_actions":{"view_html":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS","download_json":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS.json","view_paper":"https://pith.science/paper/V3AT2AKC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.00411&json=true","fetch_graph":"https://pith.science/api/pith-number/V3AT2AKCXXXDPSRGL3RO73ISBS/graph.json","fetch_events":"https://pith.science/api/pith-number/V3AT2AKCXXXDPSRGL3RO73ISBS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS/action/storage_attestation","attest_author":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS/action/author_attestation","sign_citation":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS/action/citation_signature","submit_replication":"https://pith.science/pith/V3AT2AKCXXXDPSRGL3RO73ISBS/action/replication_record"}},"created_at":"2026-07-05T12:03:20.709768+00:00","updated_at":"2026-07-05T12:03:20.709768+00:00"}