{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KAI6MG7IVM7HCSQ7US5W2K4TCP","short_pith_number":"pith:KAI6MG7I","schema_version":"1.0","canonical_sha256":"5011e61be8ab3e714a1fa4bb6d2b9313e370cc077f0af8e86d1a77829f50824a","source":{"kind":"arxiv","id":"2308.15443","version":1},"attestation_state":"computed","paper":{"title":"Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","stat.CO","stat.ML"],"primary_cat":"q-fin.ST","authors_text":"Rafa{\\l} Weron, Weronika Nitka","submitted_at":"2023-08-29T17:10:38Z","abstract_excerpt":"Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we con"},"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":"2308.15443","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2023-08-29T17:10:38Z","cross_cats_sorted":["econ.EM","stat.CO","stat.ML"],"title_canon_sha256":"0936e6e623e585db98868a8b2513e653b680a242e95e4ac58d3ef8f11d1c845b","abstract_canon_sha256":"12ab485804b37af8e2778b4d6d5b06f3009cc33d504c838f74a363900fabccc3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:45:54.642364Z","signature_b64":"scOl7tj/QjhzxXCexTUJH9iXkYoCt/vO4GNQSGJAQF8m8ifC/3PoYucmXTBv+CaswsmPNmh8b72mXGnnTIW7DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5011e61be8ab3e714a1fa4bb6d2b9313e370cc077f0af8e86d1a77829f50824a","last_reissued_at":"2026-07-05T06:45:54.641858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:45:54.641858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","stat.CO","stat.ML"],"primary_cat":"q-fin.ST","authors_text":"Rafa{\\l} Weron, Weronika Nitka","submitted_at":"2023-08-29T17:10:38Z","abstract_excerpt":"Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.15443","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/2308.15443/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":"2308.15443","created_at":"2026-07-05T06:45:54.641922+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.15443v1","created_at":"2026-07-05T06:45:54.641922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.15443","created_at":"2026-07-05T06:45:54.641922+00:00"},{"alias_kind":"pith_short_12","alias_value":"KAI6MG7IVM7H","created_at":"2026-07-05T06:45:54.641922+00:00"},{"alias_kind":"pith_short_16","alias_value":"KAI6MG7IVM7HCSQ7","created_at":"2026-07-05T06:45:54.641922+00:00"},{"alias_kind":"pith_short_8","alias_value":"KAI6MG7I","created_at":"2026-07-05T06:45:54.641922+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.10071","citing_title":"Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19580","citing_title":"Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP","json":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP.json","graph_json":"https://pith.science/api/pith-number/KAI6MG7IVM7HCSQ7US5W2K4TCP/graph.json","events_json":"https://pith.science/api/pith-number/KAI6MG7IVM7HCSQ7US5W2K4TCP/events.json","paper":"https://pith.science/paper/KAI6MG7I"},"agent_actions":{"view_html":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP","download_json":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP.json","view_paper":"https://pith.science/paper/KAI6MG7I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.15443&json=true","fetch_graph":"https://pith.science/api/pith-number/KAI6MG7IVM7HCSQ7US5W2K4TCP/graph.json","fetch_events":"https://pith.science/api/pith-number/KAI6MG7IVM7HCSQ7US5W2K4TCP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP/action/storage_attestation","attest_author":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP/action/author_attestation","sign_citation":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP/action/citation_signature","submit_replication":"https://pith.science/pith/KAI6MG7IVM7HCSQ7US5W2K4TCP/action/replication_record"}},"created_at":"2026-07-05T06:45:54.641922+00:00","updated_at":"2026-07-05T06:45:54.641922+00:00"}