{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OEIIPTA5HSXM4VKH35YA74WTUB","short_pith_number":"pith:OEIIPTA5","schema_version":"1.0","canonical_sha256":"711087cc1d3caece5547df700ff2d3a05f15e61516dcf7d3ae2ff374af4745d5","source":{"kind":"arxiv","id":"2506.08113","version":2},"attestation_state":"computed","paper":{"title":"Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-fin.ST"],"primary_cat":"cs.LG","authors_text":"Gilbert Fridgen, Igor Tchappi, Timoth\\'ee Hornek Amir Sartipi","submitted_at":"2025-06-09T18:10:00Z","abstract_excerpt":"Accurate electricity price forecasting (EPF) is crucial for effective decision-making in power trading on the spot market. While recent advances in generative artificial intelligence (GenAI) and pre-trained large language models (LLMs) have inspired the development of numerous time series foundation models (TSFMs) for time series forecasting, their effectiveness in EPF remains uncertain. To address this gap, we benchmark several state-of-the-art pretrained models--Chronos-Bolt, Chronos-T5, TimesFM, Moirai, Time-MoE, and TimeGPT--against established statistical and machine learning (ML) methods"},"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":"2506.08113","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T18:10:00Z","cross_cats_sorted":["cs.AI","q-fin.ST"],"title_canon_sha256":"ff675ffed9141f8a738b6579015d4a3d6711fb614afdd82526f8f86a40a9ee4f","abstract_canon_sha256":"f2213896f483004e069167eada4567876553b9812a2748fe4380d07ed1d43017"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:20.389010Z","signature_b64":"D/0PmP+6dZvx0uQdcPWmgV8HLzKVCQljWnlopUjX3M4hS/uFPCey1pnWJtkNljOYFpl/pF87U56VMqPc8EK1Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"711087cc1d3caece5547df700ff2d3a05f15e61516dcf7d3ae2ff374af4745d5","last_reissued_at":"2026-07-05T11:56:20.388551Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:20.388551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-fin.ST"],"primary_cat":"cs.LG","authors_text":"Gilbert Fridgen, Igor Tchappi, Timoth\\'ee Hornek Amir Sartipi","submitted_at":"2025-06-09T18:10:00Z","abstract_excerpt":"Accurate electricity price forecasting (EPF) is crucial for effective decision-making in power trading on the spot market. While recent advances in generative artificial intelligence (GenAI) and pre-trained large language models (LLMs) have inspired the development of numerous time series foundation models (TSFMs) for time series forecasting, their effectiveness in EPF remains uncertain. To address this gap, we benchmark several state-of-the-art pretrained models--Chronos-Bolt, Chronos-T5, TimesFM, Moirai, Time-MoE, and TimeGPT--against established statistical and machine learning (ML) methods"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08113","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.08113/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":"2506.08113","created_at":"2026-07-05T11:56:20.388607+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.08113v2","created_at":"2026-07-05T11:56:20.388607+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08113","created_at":"2026-07-05T11:56:20.388607+00:00"},{"alias_kind":"pith_short_12","alias_value":"OEIIPTA5HSXM","created_at":"2026-07-05T11:56:20.388607+00:00"},{"alias_kind":"pith_short_16","alias_value":"OEIIPTA5HSXM4VKH","created_at":"2026-07-05T11:56:20.388607+00:00"},{"alias_kind":"pith_short_8","alias_value":"OEIIPTA5","created_at":"2026-07-05T11:56:20.388607+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.15230","citing_title":"EnergyAgentBench: Benchmarking LLM Agents on Live Energy Infrastructure Data","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB","json":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB.json","graph_json":"https://pith.science/api/pith-number/OEIIPTA5HSXM4VKH35YA74WTUB/graph.json","events_json":"https://pith.science/api/pith-number/OEIIPTA5HSXM4VKH35YA74WTUB/events.json","paper":"https://pith.science/paper/OEIIPTA5"},"agent_actions":{"view_html":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB","download_json":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB.json","view_paper":"https://pith.science/paper/OEIIPTA5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.08113&json=true","fetch_graph":"https://pith.science/api/pith-number/OEIIPTA5HSXM4VKH35YA74WTUB/graph.json","fetch_events":"https://pith.science/api/pith-number/OEIIPTA5HSXM4VKH35YA74WTUB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB/action/storage_attestation","attest_author":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB/action/author_attestation","sign_citation":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB/action/citation_signature","submit_replication":"https://pith.science/pith/OEIIPTA5HSXM4VKH35YA74WTUB/action/replication_record"}},"created_at":"2026-07-05T11:56:20.388607+00:00","updated_at":"2026-07-05T11:56:20.388607+00:00"}