{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DLUGYV3CQGOTRLS4235ATOEWIK","short_pith_number":"pith:DLUGYV3C","schema_version":"1.0","canonical_sha256":"1ae86c5762819d38ae5cd6fa09b89642903c0082e3be9992b2f81229cb96d156","source":{"kind":"arxiv","id":"2306.05043","version":1},"attestation_state":"computed","paper":{"title":"Non-autoregressive Conditional Diffusion Models for Time Series Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"James Kwok, Lifeng Shen","submitted_at":"2023-06-08T08:53:59Z","abstract_excerpt":"Recently, denoising diffusion models have led to significant breakthroughs in the generation of images, audio and text. However, it is still an open question on how to adapt their strong modeling ability to model time series. In this paper, we propose TimeDiff, a non-autoregressive diffusion model that achieves high-quality time series prediction with the introduction of two novel conditioning mechanisms: future mixup and autoregressive initialization. Similar to teacher forcing, future mixup allows parts of the ground-truth future predictions for conditioning, while autoregressive initializat"},"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":"2306.05043","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-08T08:53:59Z","cross_cats_sorted":[],"title_canon_sha256":"b1f738bcfe99fdd578534cc406e325b0a001faaa702948c6c713e9750cee0588","abstract_canon_sha256":"8bf108c08e43eb6ac6ebb7f4d6d46a2e2cc2c68532f56d2a76893827ca2648fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:45.049984Z","signature_b64":"4Kr+v8KexvxgI/jPnHwEipiE1sd3Zm9jAshAQV2+RcFqAlYhLI6BfCWfwQhep4gYyJ28jmK818agCoeRbt9TDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ae86c5762819d38ae5cd6fa09b89642903c0082e3be9992b2f81229cb96d156","last_reissued_at":"2026-07-05T06:18:45.049641Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:45.049641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-autoregressive Conditional Diffusion Models for Time Series Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"James Kwok, Lifeng Shen","submitted_at":"2023-06-08T08:53:59Z","abstract_excerpt":"Recently, denoising diffusion models have led to significant breakthroughs in the generation of images, audio and text. However, it is still an open question on how to adapt their strong modeling ability to model time series. In this paper, we propose TimeDiff, a non-autoregressive diffusion model that achieves high-quality time series prediction with the introduction of two novel conditioning mechanisms: future mixup and autoregressive initialization. Similar to teacher forcing, future mixup allows parts of the ground-truth future predictions for conditioning, while autoregressive initializat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.05043","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/2306.05043/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":"2306.05043","created_at":"2026-07-05T06:18:45.049697+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.05043v1","created_at":"2026-07-05T06:18:45.049697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.05043","created_at":"2026-07-05T06:18:45.049697+00:00"},{"alias_kind":"pith_short_12","alias_value":"DLUGYV3CQGOT","created_at":"2026-07-05T06:18:45.049697+00:00"},{"alias_kind":"pith_short_16","alias_value":"DLUGYV3CQGOTRLS4","created_at":"2026-07-05T06:18:45.049697+00:00"},{"alias_kind":"pith_short_8","alias_value":"DLUGYV3C","created_at":"2026-07-05T06:18:45.049697+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/DLUGYV3CQGOTRLS4235ATOEWIK","json":"https://pith.science/pith/DLUGYV3CQGOTRLS4235ATOEWIK.json","graph_json":"https://pith.science/api/pith-number/DLUGYV3CQGOTRLS4235ATOEWIK/graph.json","events_json":"https://pith.science/api/pith-number/DLUGYV3CQGOTRLS4235ATOEWIK/events.json","paper":"https://pith.science/paper/DLUGYV3C"},"agent_actions":{"view_html":"https://pith.science/pith/DLUGYV3CQGOTRLS4235ATOEWIK","download_json":"https://pith.science/pith/DLUGYV3CQGOTRLS4235ATOEWIK.json","view_paper":"https://pith.science/paper/DLUGYV3C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.05043&json=true","fetch_graph":"https://pith.science/api/pith-number/DLUGYV3CQGOTRLS4235ATOEWIK/graph.json","fetch_events":"https://pith.science/api/pith-number/DLUGYV3CQGOTRLS4235ATOEWIK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DLUGYV3CQGOTRLS4235ATOEWIK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DLUGYV3CQGOTRLS4235ATOEWIK/action/storage_attestation","attest_author":"https://pith.science/pith/DLUGYV3CQGOTRLS4235ATOEWIK/action/author_attestation","sign_citation":"https://pith.science/pith/DLUGYV3CQGOTRLS4235ATOEWIK/action/citation_signature","submit_replication":"https://pith.science/pith/DLUGYV3CQGOTRLS4235ATOEWIK/action/replication_record"}},"created_at":"2026-07-05T06:18:45.049697+00:00","updated_at":"2026-07-05T06:18:45.049697+00:00"}