{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5EYFNIVYUTHJRRIT5PNILQT626","short_pith_number":"pith:5EYFNIVY","schema_version":"1.0","canonical_sha256":"e93056a2b8a4ce98c513ebda85c27ed7bb7e3d9603c9a623489785458803328e","source":{"kind":"arxiv","id":"2407.19784","version":1},"attestation_state":"computed","paper":{"title":"Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Caesar Wu, Gregoire Danoy, Jingjing Xu, Pascal Bouvry, Yuan-Fang Li","submitted_at":"2024-07-29T08:27:21Z","abstract_excerpt":"Alongside the continuous process of improving AI performance through the development of more sophisticated models, researchers have also focused their attention to the emerging concept of data-centric AI, which emphasizes the important role of data in a systematic machine learning training process. Nonetheless, the development of models has also continued apace. One result of this progress is the development of the Transformer Architecture, which possesses a high level of capability in multiple domains such as Natural Language Processing (NLP), Computer Vision (CV) and Time Series Forecasting "},"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":"2407.19784","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-29T08:27:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bc14f00db666e5df35e67f750481cb2a43f33bbfd9a70c36364e709c3800dee9","abstract_canon_sha256":"343ad68dc6294a8843de52a800597ffad1f15c60ce65d0a9639126b75d2f6baa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:49:44.512000Z","signature_b64":"VR6AaG3pvavDJBvBzlKrXuHb818YKiHu3ecsu5jEkQ3pzTbVOQw8EcijAwRnMW9x0rCBdnHMV1Wu47WSuIedAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e93056a2b8a4ce98c513ebda85c27ed7bb7e3d9603c9a623489785458803328e","last_reissued_at":"2026-07-05T08:49:44.511628Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:49:44.511628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Caesar Wu, Gregoire Danoy, Jingjing Xu, Pascal Bouvry, Yuan-Fang Li","submitted_at":"2024-07-29T08:27:21Z","abstract_excerpt":"Alongside the continuous process of improving AI performance through the development of more sophisticated models, researchers have also focused their attention to the emerging concept of data-centric AI, which emphasizes the important role of data in a systematic machine learning training process. Nonetheless, the development of models has also continued apace. One result of this progress is the development of the Transformer Architecture, which possesses a high level of capability in multiple domains such as Natural Language Processing (NLP), Computer Vision (CV) and Time Series Forecasting "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.19784","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/2407.19784/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":"2407.19784","created_at":"2026-07-05T08:49:44.511689+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.19784v1","created_at":"2026-07-05T08:49:44.511689+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.19784","created_at":"2026-07-05T08:49:44.511689+00:00"},{"alias_kind":"pith_short_12","alias_value":"5EYFNIVYUTHJ","created_at":"2026-07-05T08:49:44.511689+00:00"},{"alias_kind":"pith_short_16","alias_value":"5EYFNIVYUTHJRRIT","created_at":"2026-07-05T08:49:44.511689+00:00"},{"alias_kind":"pith_short_8","alias_value":"5EYFNIVY","created_at":"2026-07-05T08:49:44.511689+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.14507","citing_title":"Diffusion Models for Time Series Forecasting: A Survey","ref_index":71,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626","json":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626.json","graph_json":"https://pith.science/api/pith-number/5EYFNIVYUTHJRRIT5PNILQT626/graph.json","events_json":"https://pith.science/api/pith-number/5EYFNIVYUTHJRRIT5PNILQT626/events.json","paper":"https://pith.science/paper/5EYFNIVY"},"agent_actions":{"view_html":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626","download_json":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626.json","view_paper":"https://pith.science/paper/5EYFNIVY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.19784&json=true","fetch_graph":"https://pith.science/api/pith-number/5EYFNIVYUTHJRRIT5PNILQT626/graph.json","fetch_events":"https://pith.science/api/pith-number/5EYFNIVYUTHJRRIT5PNILQT626/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626/action/storage_attestation","attest_author":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626/action/author_attestation","sign_citation":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626/action/citation_signature","submit_replication":"https://pith.science/pith/5EYFNIVYUTHJRRIT5PNILQT626/action/replication_record"}},"created_at":"2026-07-05T08:49:44.511689+00:00","updated_at":"2026-07-05T08:49:44.511689+00:00"}