{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5O4RPFLG56KZWZTCC7PVPGPCI7","short_pith_number":"pith:5O4RPFLG","schema_version":"1.0","canonical_sha256":"ebb9179566ef959b666217df5799e247e8f0fd6b0702f72d71e2a0d71bfd4bb2","source":{"kind":"arxiv","id":"2409.02322","version":2},"attestation_state":"computed","paper":{"title":"TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Defu Cao, Wen Ye, Yan Liu, Yizhou Zhang","submitted_at":"2024-09-03T22:31:57Z","abstract_excerpt":"Foundation models, particularly Large Language Models (LLMs), have revolutionized text and video processing, yet time series data presents distinct challenges for such approaches due to domain-specific features such as missing values, multi-resolution characteristics, etc. Furthermore, the de-facto autoregressive transformers tend to learn deterministic temporal dependencies within pre-trained data while overlooking inherent uncertainties and lacking integration of physical constraints. In this paper, we introduce TimeDiT, a diffusion transformer model that synergistically combines transformer"},"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":"2409.02322","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T22:31:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9aeb2536b4bf7c89ad141e3ee7ab3b89bceb0323394be03c186667c6a4c1a08f","abstract_canon_sha256":"e425426d8b8c2f46c9e825d306dae0a5d95c1877fa739d3c1163ec91ca40071f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:12:19.427849Z","signature_b64":"g+Npx9EfQDbw0lJV3D4tOUfcVwNVWo2U0c4nXr1v2Fx0CUP4NyFF3AgArALxNfJXIIH+FzsDiI7MwQotuQ80AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebb9179566ef959b666217df5799e247e8f0fd6b0702f72d71e2a0d71bfd4bb2","last_reissued_at":"2026-07-05T10:12:19.427313Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:12:19.427313Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Defu Cao, Wen Ye, Yan Liu, Yizhou Zhang","submitted_at":"2024-09-03T22:31:57Z","abstract_excerpt":"Foundation models, particularly Large Language Models (LLMs), have revolutionized text and video processing, yet time series data presents distinct challenges for such approaches due to domain-specific features such as missing values, multi-resolution characteristics, etc. Furthermore, the de-facto autoregressive transformers tend to learn deterministic temporal dependencies within pre-trained data while overlooking inherent uncertainties and lacking integration of physical constraints. In this paper, we introduce TimeDiT, a diffusion transformer model that synergistically combines transformer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02322","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/2409.02322/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":"2409.02322","created_at":"2026-07-05T10:12:19.427389+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.02322v2","created_at":"2026-07-05T10:12:19.427389+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02322","created_at":"2026-07-05T10:12:19.427389+00:00"},{"alias_kind":"pith_short_12","alias_value":"5O4RPFLG56KZ","created_at":"2026-07-05T10:12:19.427389+00:00"},{"alias_kind":"pith_short_16","alias_value":"5O4RPFLG56KZWZTC","created_at":"2026-07-05T10:12:19.427389+00:00"},{"alias_kind":"pith_short_8","alias_value":"5O4RPFLG","created_at":"2026-07-05T10:12:19.427389+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26487","citing_title":"Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06285","citing_title":"TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23112","citing_title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7","json":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7.json","graph_json":"https://pith.science/api/pith-number/5O4RPFLG56KZWZTCC7PVPGPCI7/graph.json","events_json":"https://pith.science/api/pith-number/5O4RPFLG56KZWZTCC7PVPGPCI7/events.json","paper":"https://pith.science/paper/5O4RPFLG"},"agent_actions":{"view_html":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7","download_json":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7.json","view_paper":"https://pith.science/paper/5O4RPFLG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.02322&json=true","fetch_graph":"https://pith.science/api/pith-number/5O4RPFLG56KZWZTCC7PVPGPCI7/graph.json","fetch_events":"https://pith.science/api/pith-number/5O4RPFLG56KZWZTCC7PVPGPCI7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/action/storage_attestation","attest_author":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/action/author_attestation","sign_citation":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/action/citation_signature","submit_replication":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/action/replication_record"}},"created_at":"2026-07-05T10:12:19.427389+00:00","updated_at":"2026-07-05T10:12:19.427389+00:00"}