{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HDQ4MNFVVTZKAHHARREAVGMIN2","short_pith_number":"pith:HDQ4MNFV","schema_version":"1.0","canonical_sha256":"38e1c634b5acf2a01ce08c480a99886e8ca3d41970938d4fd0c487b39d645f67","source":{"kind":"arxiv","id":"2305.10721","version":2},"attestation_state":"computed","paper":{"title":"Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Shiyi Qi, Yiduo Li, Zenglin Xu, Zhe Li","submitted_at":"2023-05-18T05:39:46Z","abstract_excerpt":"Introduction: Long-term time series forecasting (LTSF) has gained significant attention in recent years. While various specialized designs exist for capturing temporal dependency, recent studies have shown that even a single linear layer can achieve competitive performance. This paper investigates the intrinsic effectiveness of recent LTSF approaches and reveals the critical role of affine mapping.\n  Materials and methods: We conduct comprehensive experiments on both simulated and real-world datasets to analyze the components of state-of-the-art models. A theoretical analysis is provided to ex"},"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":"2305.10721","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-18T05:39:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"43b2ee25c65d60f1d2376424a5743a48a3e970319896accd005e119b07e70ee4","abstract_canon_sha256":"13434af6c447ee8df1f1abfaa5ba9fe3be29480cd2f2d54f318078060650f4ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:02:45.019888Z","signature_b64":"cQIOGwTS1h8ewuFYTkKl0s1TlUyjaMnJtZfb1CKhcv73+Qeada5wnIr09Go7rLEJ+hrmOkDt5lu5FoE5/Pz9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38e1c634b5acf2a01ce08c480a99886e8ca3d41970938d4fd0c487b39d645f67","last_reissued_at":"2026-05-20T00:02:45.019150Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:02:45.019150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Shiyi Qi, Yiduo Li, Zenglin Xu, Zhe Li","submitted_at":"2023-05-18T05:39:46Z","abstract_excerpt":"Introduction: Long-term time series forecasting (LTSF) has gained significant attention in recent years. While various specialized designs exist for capturing temporal dependency, recent studies have shown that even a single linear layer can achieve competitive performance. This paper investigates the intrinsic effectiveness of recent LTSF approaches and reveals the critical role of affine mapping.\n  Materials and methods: We conduct comprehensive experiments on both simulated and real-world datasets to analyze the components of state-of-the-art models. A theoretical analysis is provided to ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.10721","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/2305.10721/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":"2305.10721","created_at":"2026-05-20T00:02:45.019265+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.10721v2","created_at":"2026-05-20T00:02:45.019265+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.10721","created_at":"2026-05-20T00:02:45.019265+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDQ4MNFVVTZK","created_at":"2026-05-20T00:02:45.019265+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDQ4MNFVVTZKAHHA","created_at":"2026-05-20T00:02:45.019265+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDQ4MNFV","created_at":"2026-05-20T00:02:45.019265+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":7,"sample":[{"citing_arxiv_id":"2509.23597","citing_title":"Characteristic Root Analysis and Regularization for Linear Time Series Forecasting","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2604.16325","citing_title":"UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2501.00663","citing_title":"Titans: Learning to Memorize at Test Time","ref_index":64,"is_internal_anchor":true},{"citing_arxiv_id":"2310.06625","citing_title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10292","citing_title":"LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling","ref_index":107,"is_internal_anchor":true},{"citing_arxiv_id":"2605.09208","citing_title":"TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting","ref_index":43,"is_internal_anchor":true},{"citing_arxiv_id":"2604.23474","citing_title":"GeoCert: Certified Geometric AI for Reliable Forecasting","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2","json":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2.json","graph_json":"https://pith.science/api/pith-number/HDQ4MNFVVTZKAHHARREAVGMIN2/graph.json","events_json":"https://pith.science/api/pith-number/HDQ4MNFVVTZKAHHARREAVGMIN2/events.json","paper":"https://pith.science/paper/HDQ4MNFV"},"agent_actions":{"view_html":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2","download_json":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2.json","view_paper":"https://pith.science/paper/HDQ4MNFV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.10721&json=true","fetch_graph":"https://pith.science/api/pith-number/HDQ4MNFVVTZKAHHARREAVGMIN2/graph.json","fetch_events":"https://pith.science/api/pith-number/HDQ4MNFVVTZKAHHARREAVGMIN2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2/action/storage_attestation","attest_author":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2/action/author_attestation","sign_citation":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2/action/citation_signature","submit_replication":"https://pith.science/pith/HDQ4MNFVVTZKAHHARREAVGMIN2/action/replication_record"}},"created_at":"2026-05-20T00:02:45.019265+00:00","updated_at":"2026-05-20T00:02:45.019265+00:00"}