{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MKQORXLISTZS7TE7SCOL7Z43GZ","short_pith_number":"pith:MKQORXLI","schema_version":"1.0","canonical_sha256":"62a0e8dd6894f32fcc9f909cbfe79b365284476bc73dd5b8a7e0f1408d8ec81c","source":{"kind":"arxiv","id":"2412.02722","version":1},"attestation_state":"computed","paper":{"title":"Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Boris N. Oreshkin, Grzegorz Dudek, Mateusz Kasprzyk, Pawe{\\l} Pe{\\l}ka","submitted_at":"2024-12-02T19:31:44Z","abstract_excerpt":"This paper presents an enhanced N-BEATS model, N-BEATS*, for improved mid-term electricity load forecasting (MTLF). Building on the strengths of the original N-BEATS architecture, which excels in handling complex time series data without requiring preprocessing or domain-specific knowledge, N-BEATS* introduces two key modifications. (1) A novel loss function -- combining pinball loss based on MAPE with normalized MSE, the new loss function allows for a more balanced approach by capturing both L1 and L2 loss terms. (2) A modified block architecture -- the internal structure of the N-BEATS block"},"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":"2412.02722","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-02T19:31:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6310241faece6decbe0fdebae4432381baa0d8077a262d8daf2e2e9e4b3922a4","abstract_canon_sha256":"60e08e63551a99f6285808cea0b98dce9445790f37ae4fe820f94411c1e40a54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:18.599527Z","signature_b64":"aZJEWiyPWl532u2z9LTrthd08Q4uhZXD/3MlfPCVsdR55O9+nVJa4JKPKqufrTz65/y/p0YmS7OazRu1sgR1CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62a0e8dd6894f32fcc9f909cbfe79b365284476bc73dd5b8a7e0f1408d8ec81c","last_reissued_at":"2026-07-05T09:44:18.599058Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:18.599058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Boris N. Oreshkin, Grzegorz Dudek, Mateusz Kasprzyk, Pawe{\\l} Pe{\\l}ka","submitted_at":"2024-12-02T19:31:44Z","abstract_excerpt":"This paper presents an enhanced N-BEATS model, N-BEATS*, for improved mid-term electricity load forecasting (MTLF). Building on the strengths of the original N-BEATS architecture, which excels in handling complex time series data without requiring preprocessing or domain-specific knowledge, N-BEATS* introduces two key modifications. (1) A novel loss function -- combining pinball loss based on MAPE with normalized MSE, the new loss function allows for a more balanced approach by capturing both L1 and L2 loss terms. (2) A modified block architecture -- the internal structure of the N-BEATS block"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.02722","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/2412.02722/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":"2412.02722","created_at":"2026-07-05T09:44:18.599118+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.02722v1","created_at":"2026-07-05T09:44:18.599118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.02722","created_at":"2026-07-05T09:44:18.599118+00:00"},{"alias_kind":"pith_short_12","alias_value":"MKQORXLISTZS","created_at":"2026-07-05T09:44:18.599118+00:00"},{"alias_kind":"pith_short_16","alias_value":"MKQORXLISTZS7TE7","created_at":"2026-07-05T09:44:18.599118+00:00"},{"alias_kind":"pith_short_8","alias_value":"MKQORXLI","created_at":"2026-07-05T09:44:18.599118+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/MKQORXLISTZS7TE7SCOL7Z43GZ","json":"https://pith.science/pith/MKQORXLISTZS7TE7SCOL7Z43GZ.json","graph_json":"https://pith.science/api/pith-number/MKQORXLISTZS7TE7SCOL7Z43GZ/graph.json","events_json":"https://pith.science/api/pith-number/MKQORXLISTZS7TE7SCOL7Z43GZ/events.json","paper":"https://pith.science/paper/MKQORXLI"},"agent_actions":{"view_html":"https://pith.science/pith/MKQORXLISTZS7TE7SCOL7Z43GZ","download_json":"https://pith.science/pith/MKQORXLISTZS7TE7SCOL7Z43GZ.json","view_paper":"https://pith.science/paper/MKQORXLI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.02722&json=true","fetch_graph":"https://pith.science/api/pith-number/MKQORXLISTZS7TE7SCOL7Z43GZ/graph.json","fetch_events":"https://pith.science/api/pith-number/MKQORXLISTZS7TE7SCOL7Z43GZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MKQORXLISTZS7TE7SCOL7Z43GZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MKQORXLISTZS7TE7SCOL7Z43GZ/action/storage_attestation","attest_author":"https://pith.science/pith/MKQORXLISTZS7TE7SCOL7Z43GZ/action/author_attestation","sign_citation":"https://pith.science/pith/MKQORXLISTZS7TE7SCOL7Z43GZ/action/citation_signature","submit_replication":"https://pith.science/pith/MKQORXLISTZS7TE7SCOL7Z43GZ/action/replication_record"}},"created_at":"2026-07-05T09:44:18.599118+00:00","updated_at":"2026-07-05T09:44:18.599118+00:00"}