{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:FEKBPR3HCH3QM4YEQ5YLJN745I","short_pith_number":"pith:FEKBPR3H","schema_version":"1.0","canonical_sha256":"291417c76711f70673048770b4b7fcea1f55b3a9d60d26f241dbbf6eb72c54dd","source":{"kind":"arxiv","id":"2004.11834","version":1},"attestation_state":"computed","paper":{"title":"Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.SP","authors_text":"Grzegorz Dudek, Pawe{\\l} Pe{\\l}ka","submitted_at":"2020-04-22T08:39:32Z","abstract_excerpt":"This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. Pattern representation simplifies the complex nonlinear and nonstationary time series, filtering out the trend and equalizing variance. Two types of patterns are defined: x-pattern and y-pattern. The former requires additional forecasting for the coding variables. The latter determines the coding variables from the process history."},"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":"2004.11834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-04-22T08:39:32Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"dbbae4320df9b2d0079fd496aa0e8edd5c11453b2402f2a58f6079335d0fd900","abstract_canon_sha256":"ca1449458fc74fdc8fccb25c67e511b14329b2fd0a55ced0b1a7785052d22add"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:58:54.294841Z","signature_b64":"lAj5QdYYtJqS4R8hTSPyEi6f3dLL26tdWar4/GJFXEpmVIdAArPaExH9Iyf5cxSirCABKJ9WapjNrEod3crkAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"291417c76711f70673048770b4b7fcea1f55b3a9d60d26f241dbbf6eb72c54dd","last_reissued_at":"2026-07-05T00:58:54.294417Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:58:54.294417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.SP","authors_text":"Grzegorz Dudek, Pawe{\\l} Pe{\\l}ka","submitted_at":"2020-04-22T08:39:32Z","abstract_excerpt":"This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. Pattern representation simplifies the complex nonlinear and nonstationary time series, filtering out the trend and equalizing variance. Two types of patterns are defined: x-pattern and y-pattern. The former requires additional forecasting for the coding variables. The latter determines the coding variables from the process history."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.11834","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/2004.11834/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":"2004.11834","created_at":"2026-07-05T00:58:54.294493+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.11834v1","created_at":"2026-07-05T00:58:54.294493+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.11834","created_at":"2026-07-05T00:58:54.294493+00:00"},{"alias_kind":"pith_short_12","alias_value":"FEKBPR3HCH3Q","created_at":"2026-07-05T00:58:54.294493+00:00"},{"alias_kind":"pith_short_16","alias_value":"FEKBPR3HCH3QM4YE","created_at":"2026-07-05T00:58:54.294493+00:00"},{"alias_kind":"pith_short_8","alias_value":"FEKBPR3H","created_at":"2026-07-05T00:58:54.294493+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/FEKBPR3HCH3QM4YEQ5YLJN745I","json":"https://pith.science/pith/FEKBPR3HCH3QM4YEQ5YLJN745I.json","graph_json":"https://pith.science/api/pith-number/FEKBPR3HCH3QM4YEQ5YLJN745I/graph.json","events_json":"https://pith.science/api/pith-number/FEKBPR3HCH3QM4YEQ5YLJN745I/events.json","paper":"https://pith.science/paper/FEKBPR3H"},"agent_actions":{"view_html":"https://pith.science/pith/FEKBPR3HCH3QM4YEQ5YLJN745I","download_json":"https://pith.science/pith/FEKBPR3HCH3QM4YEQ5YLJN745I.json","view_paper":"https://pith.science/paper/FEKBPR3H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.11834&json=true","fetch_graph":"https://pith.science/api/pith-number/FEKBPR3HCH3QM4YEQ5YLJN745I/graph.json","fetch_events":"https://pith.science/api/pith-number/FEKBPR3HCH3QM4YEQ5YLJN745I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FEKBPR3HCH3QM4YEQ5YLJN745I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FEKBPR3HCH3QM4YEQ5YLJN745I/action/storage_attestation","attest_author":"https://pith.science/pith/FEKBPR3HCH3QM4YEQ5YLJN745I/action/author_attestation","sign_citation":"https://pith.science/pith/FEKBPR3HCH3QM4YEQ5YLJN745I/action/citation_signature","submit_replication":"https://pith.science/pith/FEKBPR3HCH3QM4YEQ5YLJN745I/action/replication_record"}},"created_at":"2026-07-05T00:58:54.294493+00:00","updated_at":"2026-07-05T00:58:54.294493+00:00"}