{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:UKNRYQPHUGNSKGUIQR6NO6CJG4","short_pith_number":"pith:UKNRYQPH","schema_version":"1.0","canonical_sha256":"a29b1c41e7a19b251a88847cd77849371be7bc522964a5c7692e2b300abf7270","source":{"kind":"arxiv","id":"1506.06972","version":1},"attestation_state":"computed","paper":{"title":"GEFCOM 2014 - Probabilistic Electricity Price Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Gabor Nagy, Gergo Barta, Gyula Borbely, Sandor Kazi, Tamas Henk","submitted_at":"2015-06-23T12:27:50Z","abstract_excerpt":"Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and auto-correlation out-of-the box and achieve "},"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":"1506.06972","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-23T12:27:50Z","cross_cats_sorted":["cs.CE","cs.LG","stat.AP"],"title_canon_sha256":"bd14dfe555b60c508693fc6fb421e13a53a72f7c4862717838cfc05bbf1a5d20","abstract_canon_sha256":"ac38f6444ef1719c5c53fb10923f536720e0fab8c8672ca31bcf71707bfcad65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:40:55.434139Z","signature_b64":"y/USVmR103fa5IEauQBZemYXqSeInbLs0I2UF2s/wTmKuTzsYds3Es4I+IIYNdBD6BUnd4YHp2UdiimCdjQyCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a29b1c41e7a19b251a88847cd77849371be7bc522964a5c7692e2b300abf7270","last_reissued_at":"2026-05-18T01:40:55.433551Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:40:55.433551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GEFCOM 2014 - Probabilistic Electricity Price Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Gabor Nagy, Gergo Barta, Gyula Borbely, Sandor Kazi, Tamas Henk","submitted_at":"2015-06-23T12:27:50Z","abstract_excerpt":"Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and auto-correlation out-of-the box and achieve "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.06972","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":""},"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":"1506.06972","created_at":"2026-05-18T01:40:55.433649+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.06972v1","created_at":"2026-05-18T01:40:55.433649+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.06972","created_at":"2026-05-18T01:40:55.433649+00:00"},{"alias_kind":"pith_short_12","alias_value":"UKNRYQPHUGNS","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_16","alias_value":"UKNRYQPHUGNSKGUI","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_8","alias_value":"UKNRYQPH","created_at":"2026-05-18T12:29:44.643036+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/UKNRYQPHUGNSKGUIQR6NO6CJG4","json":"https://pith.science/pith/UKNRYQPHUGNSKGUIQR6NO6CJG4.json","graph_json":"https://pith.science/api/pith-number/UKNRYQPHUGNSKGUIQR6NO6CJG4/graph.json","events_json":"https://pith.science/api/pith-number/UKNRYQPHUGNSKGUIQR6NO6CJG4/events.json","paper":"https://pith.science/paper/UKNRYQPH"},"agent_actions":{"view_html":"https://pith.science/pith/UKNRYQPHUGNSKGUIQR6NO6CJG4","download_json":"https://pith.science/pith/UKNRYQPHUGNSKGUIQR6NO6CJG4.json","view_paper":"https://pith.science/paper/UKNRYQPH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.06972&json=true","fetch_graph":"https://pith.science/api/pith-number/UKNRYQPHUGNSKGUIQR6NO6CJG4/graph.json","fetch_events":"https://pith.science/api/pith-number/UKNRYQPHUGNSKGUIQR6NO6CJG4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UKNRYQPHUGNSKGUIQR6NO6CJG4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UKNRYQPHUGNSKGUIQR6NO6CJG4/action/storage_attestation","attest_author":"https://pith.science/pith/UKNRYQPHUGNSKGUIQR6NO6CJG4/action/author_attestation","sign_citation":"https://pith.science/pith/UKNRYQPHUGNSKGUIQR6NO6CJG4/action/citation_signature","submit_replication":"https://pith.science/pith/UKNRYQPHUGNSKGUIQR6NO6CJG4/action/replication_record"}},"created_at":"2026-05-18T01:40:55.433649+00:00","updated_at":"2026-05-18T01:40:55.433649+00:00"}