{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TDSG3WREQ2QFA2A2HXWYU76UWO","short_pith_number":"pith:TDSG3WRE","schema_version":"1.0","canonical_sha256":"98e46dda2486a050681a3ded8a7fd4b3bce34a29bcac6a04f2ac89bd8d7a4c59","source":{"kind":"arxiv","id":"2404.18273","version":1},"attestation_state":"computed","paper":{"title":"Kernel Corrector LSTM","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Carlos Soares, Jo\\~ao Mendes-Moreira, Rodrigo Tuna, Yassine Baghoussi","submitted_at":"2024-04-28T18:44:10Z","abstract_excerpt":"Forecasting methods are affected by data quality issues in two ways: 1. they are hard to predict, and 2. they may affect the model negatively when it is updated with new data. The latter issue is usually addressed by pre-processing the data to remove those issues. An alternative approach has recently been proposed, Corrector LSTM (cLSTM), which is a Read \\& Write Machine Learning (RW-ML) algorithm that changes the data while learning to improve its predictions. Despite promising results being reported, cLSTM is computationally expensive, as it uses a meta-learner to monitor the hidden states o"},"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":"2404.18273","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-28T18:44:10Z","cross_cats_sorted":[],"title_canon_sha256":"8c76c4907c3259062980a875bdca650273cac48806f6541d1d36db4744c6f122","abstract_canon_sha256":"86f2bf6be424ed0baaea083c3b1f272dfc3e55a18f87f6f27eed467b0d8b514f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:13:00.755595Z","signature_b64":"04HuBJMXOsPTicZOw4HpZWes275snsKbBWvEX4n73tGsfhhbHdaG/sJoOn7FRKjBRI6seLNmPn2iwzFOgXVWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98e46dda2486a050681a3ded8a7fd4b3bce34a29bcac6a04f2ac89bd8d7a4c59","last_reissued_at":"2026-07-05T08:13:00.755185Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:13:00.755185Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kernel Corrector LSTM","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Carlos Soares, Jo\\~ao Mendes-Moreira, Rodrigo Tuna, Yassine Baghoussi","submitted_at":"2024-04-28T18:44:10Z","abstract_excerpt":"Forecasting methods are affected by data quality issues in two ways: 1. they are hard to predict, and 2. they may affect the model negatively when it is updated with new data. The latter issue is usually addressed by pre-processing the data to remove those issues. An alternative approach has recently been proposed, Corrector LSTM (cLSTM), which is a Read \\& Write Machine Learning (RW-ML) algorithm that changes the data while learning to improve its predictions. Despite promising results being reported, cLSTM is computationally expensive, as it uses a meta-learner to monitor the hidden states o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.18273","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/2404.18273/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":"2404.18273","created_at":"2026-07-05T08:13:00.755237+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.18273v1","created_at":"2026-07-05T08:13:00.755237+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.18273","created_at":"2026-07-05T08:13:00.755237+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDSG3WREQ2QF","created_at":"2026-07-05T08:13:00.755237+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDSG3WREQ2QFA2A2","created_at":"2026-07-05T08:13:00.755237+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDSG3WRE","created_at":"2026-07-05T08:13:00.755237+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/TDSG3WREQ2QFA2A2HXWYU76UWO","json":"https://pith.science/pith/TDSG3WREQ2QFA2A2HXWYU76UWO.json","graph_json":"https://pith.science/api/pith-number/TDSG3WREQ2QFA2A2HXWYU76UWO/graph.json","events_json":"https://pith.science/api/pith-number/TDSG3WREQ2QFA2A2HXWYU76UWO/events.json","paper":"https://pith.science/paper/TDSG3WRE"},"agent_actions":{"view_html":"https://pith.science/pith/TDSG3WREQ2QFA2A2HXWYU76UWO","download_json":"https://pith.science/pith/TDSG3WREQ2QFA2A2HXWYU76UWO.json","view_paper":"https://pith.science/paper/TDSG3WRE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.18273&json=true","fetch_graph":"https://pith.science/api/pith-number/TDSG3WREQ2QFA2A2HXWYU76UWO/graph.json","fetch_events":"https://pith.science/api/pith-number/TDSG3WREQ2QFA2A2HXWYU76UWO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDSG3WREQ2QFA2A2HXWYU76UWO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDSG3WREQ2QFA2A2HXWYU76UWO/action/storage_attestation","attest_author":"https://pith.science/pith/TDSG3WREQ2QFA2A2HXWYU76UWO/action/author_attestation","sign_citation":"https://pith.science/pith/TDSG3WREQ2QFA2A2HXWYU76UWO/action/citation_signature","submit_replication":"https://pith.science/pith/TDSG3WREQ2QFA2A2HXWYU76UWO/action/replication_record"}},"created_at":"2026-07-05T08:13:00.755237+00:00","updated_at":"2026-07-05T08:13:00.755237+00:00"}