{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4UNRWPZSGKYHFHFXTKU4GTOXMO","short_pith_number":"pith:4UNRWPZS","schema_version":"1.0","canonical_sha256":"e51b1b3f3232b0729cb79aa9c34dd763bc386fc3dc3f43f50d664ecdc66c0976","source":{"kind":"arxiv","id":"2409.18479","version":2},"attestation_state":"computed","paper":{"title":"CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haocheng Zhong, Ruichao Mo, Shengsheng Lin, Weiwei Lin, Wentai Wu, Xinyi Hu","submitted_at":"2024-09-27T06:42:22Z","abstract_excerpt":"The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models in long-term time series forecasting (LTSF) tasks. Specifically, we introduce the Residual Cycle Forecasting (RCF) technique, which utilizes learnable recurrent cycles to model the inherent periodic patterns within sequences, and then performs predictions on the residual components of the modeled cycles. Combining RCF with a Linear layer or a shallow MLP form"},"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":"2409.18479","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-27T06:42:22Z","cross_cats_sorted":[],"title_canon_sha256":"2e9f74c3f9fdb0cd9d731a1b7c7ccb9de56caa772d30fb3826324a8163f45b70","abstract_canon_sha256":"aba6a3d7a315447aacbb26618646b0ea668f8d7c5011bc66bb2ddbace44cdaa4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:48.363855Z","signature_b64":"aJx8IKqLtNkEnbKc0EPo0CTYNf6tZsKzqwzLpkoptucFNxUUBMhBzHvX7uu7j0qnKtuaXODGY8dLCPMdHhLyCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e51b1b3f3232b0729cb79aa9c34dd763bc386fc3dc3f43f50d664ecdc66c0976","last_reissued_at":"2026-07-05T09:20:48.363480Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:48.363480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haocheng Zhong, Ruichao Mo, Shengsheng Lin, Weiwei Lin, Wentai Wu, Xinyi Hu","submitted_at":"2024-09-27T06:42:22Z","abstract_excerpt":"The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models in long-term time series forecasting (LTSF) tasks. Specifically, we introduce the Residual Cycle Forecasting (RCF) technique, which utilizes learnable recurrent cycles to model the inherent periodic patterns within sequences, and then performs predictions on the residual components of the modeled cycles. Combining RCF with a Linear layer or a shallow MLP form"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.18479","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/2409.18479/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":"2409.18479","created_at":"2026-07-05T09:20:48.363532+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.18479v2","created_at":"2026-07-05T09:20:48.363532+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.18479","created_at":"2026-07-05T09:20:48.363532+00:00"},{"alias_kind":"pith_short_12","alias_value":"4UNRWPZSGKYH","created_at":"2026-07-05T09:20:48.363532+00:00"},{"alias_kind":"pith_short_16","alias_value":"4UNRWPZSGKYHFHFX","created_at":"2026-07-05T09:20:48.363532+00:00"},{"alias_kind":"pith_short_8","alias_value":"4UNRWPZS","created_at":"2026-07-05T09:20:48.363532+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08234","citing_title":"RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO","json":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO.json","graph_json":"https://pith.science/api/pith-number/4UNRWPZSGKYHFHFXTKU4GTOXMO/graph.json","events_json":"https://pith.science/api/pith-number/4UNRWPZSGKYHFHFXTKU4GTOXMO/events.json","paper":"https://pith.science/paper/4UNRWPZS"},"agent_actions":{"view_html":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO","download_json":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO.json","view_paper":"https://pith.science/paper/4UNRWPZS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.18479&json=true","fetch_graph":"https://pith.science/api/pith-number/4UNRWPZSGKYHFHFXTKU4GTOXMO/graph.json","fetch_events":"https://pith.science/api/pith-number/4UNRWPZSGKYHFHFXTKU4GTOXMO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO/action/storage_attestation","attest_author":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO/action/author_attestation","sign_citation":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO/action/citation_signature","submit_replication":"https://pith.science/pith/4UNRWPZSGKYHFHFXTKU4GTOXMO/action/replication_record"}},"created_at":"2026-07-05T09:20:48.363532+00:00","updated_at":"2026-07-05T09:20:48.363532+00:00"}