{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NDNNQGQH66MM3MSRWGTY57XG7L","short_pith_number":"pith:NDNNQGQH","schema_version":"1.0","canonical_sha256":"68dad81a07f798cdb251b1a78efee6fafa18a0f0e3195ea65ef4107b56f1da0c","source":{"kind":"arxiv","id":"1801.01535","version":2},"attestation_state":"computed","paper":{"title":"Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"stat.AP","authors_text":"Caisheng Wang, Carol Miller, Chang Fu, Zhongyang Zhao","submitted_at":"2018-01-04T20:12:23Z","abstract_excerpt":"Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texa"},"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":"1801.01535","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-01-04T20:12:23Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"3d1fd09bd8a7656d38d3100c66469c2fa4135afd467eec78f46975d76b2d8811","abstract_canon_sha256":"9444b2af6cdbc48075f8b40a4a9e06664919668d08d8147cfbdad5e6586e8d58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:44.256819Z","signature_b64":"tgytrOCYKMrJKzkPM3qSFkKUO2mBKXQaY8lyQsjZZm6fpDC6hdCz39vDXE7//M20bZAeZJQjO7cPWFDfzUVJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68dad81a07f798cdb251b1a78efee6fafa18a0f0e3195ea65ef4107b56f1da0c","last_reissued_at":"2026-05-18T00:22:44.256183Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:44.256183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"stat.AP","authors_text":"Caisheng Wang, Carol Miller, Chang Fu, Zhongyang Zhao","submitted_at":"2018-01-04T20:12:23Z","abstract_excerpt":"Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.01535","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":""},"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":"1801.01535","created_at":"2026-05-18T00:22:44.256285+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.01535v2","created_at":"2026-05-18T00:22:44.256285+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.01535","created_at":"2026-05-18T00:22:44.256285+00:00"},{"alias_kind":"pith_short_12","alias_value":"NDNNQGQH66MM","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NDNNQGQH66MM3MSR","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NDNNQGQH","created_at":"2026-05-18T12:32:40.477152+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/NDNNQGQH66MM3MSRWGTY57XG7L","json":"https://pith.science/pith/NDNNQGQH66MM3MSRWGTY57XG7L.json","graph_json":"https://pith.science/api/pith-number/NDNNQGQH66MM3MSRWGTY57XG7L/graph.json","events_json":"https://pith.science/api/pith-number/NDNNQGQH66MM3MSRWGTY57XG7L/events.json","paper":"https://pith.science/paper/NDNNQGQH"},"agent_actions":{"view_html":"https://pith.science/pith/NDNNQGQH66MM3MSRWGTY57XG7L","download_json":"https://pith.science/pith/NDNNQGQH66MM3MSRWGTY57XG7L.json","view_paper":"https://pith.science/paper/NDNNQGQH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.01535&json=true","fetch_graph":"https://pith.science/api/pith-number/NDNNQGQH66MM3MSRWGTY57XG7L/graph.json","fetch_events":"https://pith.science/api/pith-number/NDNNQGQH66MM3MSRWGTY57XG7L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NDNNQGQH66MM3MSRWGTY57XG7L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NDNNQGQH66MM3MSRWGTY57XG7L/action/storage_attestation","attest_author":"https://pith.science/pith/NDNNQGQH66MM3MSRWGTY57XG7L/action/author_attestation","sign_citation":"https://pith.science/pith/NDNNQGQH66MM3MSRWGTY57XG7L/action/citation_signature","submit_replication":"https://pith.science/pith/NDNNQGQH66MM3MSRWGTY57XG7L/action/replication_record"}},"created_at":"2026-05-18T00:22:44.256285+00:00","updated_at":"2026-05-18T00:22:44.256285+00:00"}