{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QN2NZG6YNWDVHQ7ETAUK5FCE75","short_pith_number":"pith:QN2NZG6Y","schema_version":"1.0","canonical_sha256":"8374dc9bd86d8753c3e49828ae9444ff484d9fd06de5e59e17f95b5fe2cc5316","source":{"kind":"arxiv","id":"1804.06776","version":1},"attestation_state":"computed","paper":{"title":"Improving Long-Horizon Forecasts with Expectation-Biased LSTM Networks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aya Abdelsalam Ismail, H\\'ector Corrada Bravo, Timothy Wood","submitted_at":"2018-04-18T15:05:44Z","abstract_excerpt":"State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN) based on Long-Short Term Memory (LSTM) cells have shown exceptional performance targeting short-horizon forecasts, e.g given a set of predictor features, forecast a target value for the next few time steps in the future. However, in many applica- tions, the performance of these methods decays as the forecasting horizon extends beyond these few time steps. This paper aims to explore the challenges of long-horizon forecasting using LSTM networks. Here, we illustrate the long-horizon forecasting problem in datasets from "},"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":"1804.06776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2018-04-18T15:05:44Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5ac524ff6a70a98759d4d14d489eab0fc2b3e94afc463909734dea95f0907320","abstract_canon_sha256":"fe589c44cb4e9c2c7358826c561b3c6aec6c4b35b9e55332c13d819771fd54a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:05.760334Z","signature_b64":"K9R2rYVjUJXZVIn3+4TV0N7fb4cKsMpYQtYpF7ttSEBV9rBxWYJivIQW/WjiI2aGI06PZNZNsmDU4dGjtollCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8374dc9bd86d8753c3e49828ae9444ff484d9fd06de5e59e17f95b5fe2cc5316","last_reissued_at":"2026-05-18T00:18:05.759793Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:05.759793Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Long-Horizon Forecasts with Expectation-Biased LSTM Networks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aya Abdelsalam Ismail, H\\'ector Corrada Bravo, Timothy Wood","submitted_at":"2018-04-18T15:05:44Z","abstract_excerpt":"State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN) based on Long-Short Term Memory (LSTM) cells have shown exceptional performance targeting short-horizon forecasts, e.g given a set of predictor features, forecast a target value for the next few time steps in the future. However, in many applica- tions, the performance of these methods decays as the forecasting horizon extends beyond these few time steps. This paper aims to explore the challenges of long-horizon forecasting using LSTM networks. Here, we illustrate the long-horizon forecasting problem in datasets from "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06776","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":"1804.06776","created_at":"2026-05-18T00:18:05.759869+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.06776v1","created_at":"2026-05-18T00:18:05.759869+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06776","created_at":"2026-05-18T00:18:05.759869+00:00"},{"alias_kind":"pith_short_12","alias_value":"QN2NZG6YNWDV","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QN2NZG6YNWDVHQ7E","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QN2NZG6Y","created_at":"2026-05-18T12:32:46.962924+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/QN2NZG6YNWDVHQ7ETAUK5FCE75","json":"https://pith.science/pith/QN2NZG6YNWDVHQ7ETAUK5FCE75.json","graph_json":"https://pith.science/api/pith-number/QN2NZG6YNWDVHQ7ETAUK5FCE75/graph.json","events_json":"https://pith.science/api/pith-number/QN2NZG6YNWDVHQ7ETAUK5FCE75/events.json","paper":"https://pith.science/paper/QN2NZG6Y"},"agent_actions":{"view_html":"https://pith.science/pith/QN2NZG6YNWDVHQ7ETAUK5FCE75","download_json":"https://pith.science/pith/QN2NZG6YNWDVHQ7ETAUK5FCE75.json","view_paper":"https://pith.science/paper/QN2NZG6Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.06776&json=true","fetch_graph":"https://pith.science/api/pith-number/QN2NZG6YNWDVHQ7ETAUK5FCE75/graph.json","fetch_events":"https://pith.science/api/pith-number/QN2NZG6YNWDVHQ7ETAUK5FCE75/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QN2NZG6YNWDVHQ7ETAUK5FCE75/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QN2NZG6YNWDVHQ7ETAUK5FCE75/action/storage_attestation","attest_author":"https://pith.science/pith/QN2NZG6YNWDVHQ7ETAUK5FCE75/action/author_attestation","sign_citation":"https://pith.science/pith/QN2NZG6YNWDVHQ7ETAUK5FCE75/action/citation_signature","submit_replication":"https://pith.science/pith/QN2NZG6YNWDVHQ7ETAUK5FCE75/action/replication_record"}},"created_at":"2026-05-18T00:18:05.759869+00:00","updated_at":"2026-05-18T00:18:05.759869+00:00"}