{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HHUYZFKECVXVXNJGD4NC5E5PAC","short_pith_number":"pith:HHUYZFKE","schema_version":"1.0","canonical_sha256":"39e98c9544156f5bb5261f1a2e93af0082ad739fd41cb7d053e2245dd57ef848","source":{"kind":"arxiv","id":"2512.15116","version":2},"attestation_state":"computed","paper":{"title":"FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Binghao Li, Hanchen Wang, Runze Li, Wenjie Zhang, Xuemin Lin, Ying Zhang, Yu Zhang","submitted_at":"2025-12-17T06:16:31Z","abstract_excerpt":"Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sensor failures and irregular sampling lead to pervasive missing values. However, existing Transformer- and diffusion-based models lack explicit inductive biases and frequency awareness, limiting their generalization under structured missing patterns and distribution shifts. We propose FADTI, a diffusion-based framework that injects frequency-informed feature modulation via a learnable Fourier Bias Projection (FBP) module and combines it with temporal model"},"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":"2512.15116","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-12-17T06:16:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bdcbf1ba159e50b919dff4e5ceb65d15644f98fd2544eb57966a50153cbfb4d4","abstract_canon_sha256":"deae02919caa14d35c69d4e00b9cee7630fc11ebeb5d4291fe70181821401662"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:11.855954Z","signature_b64":"2DIrZ6KFYl0MNoP09tQm56FxSbJy4XRlFBglkroQRW43tvwM4jv+t55W7/UoZJdnK+BULbgklEMjbDHQCLSvAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39e98c9544156f5bb5261f1a2e93af0082ad739fd41cb7d053e2245dd57ef848","last_reissued_at":"2026-06-09T01:05:11.855446Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:11.855446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Binghao Li, Hanchen Wang, Runze Li, Wenjie Zhang, Xuemin Lin, Ying Zhang, Yu Zhang","submitted_at":"2025-12-17T06:16:31Z","abstract_excerpt":"Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sensor failures and irregular sampling lead to pervasive missing values. However, existing Transformer- and diffusion-based models lack explicit inductive biases and frequency awareness, limiting their generalization under structured missing patterns and distribution shifts. We propose FADTI, a diffusion-based framework that injects frequency-informed feature modulation via a learnable Fourier Bias Projection (FBP) module and combines it with temporal model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.15116","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/2512.15116/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":"2512.15116","created_at":"2026-06-09T01:05:11.855510+00:00"},{"alias_kind":"arxiv_version","alias_value":"2512.15116v2","created_at":"2026-06-09T01:05:11.855510+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.15116","created_at":"2026-06-09T01:05:11.855510+00:00"},{"alias_kind":"pith_short_12","alias_value":"HHUYZFKECVXV","created_at":"2026-06-09T01:05:11.855510+00:00"},{"alias_kind":"pith_short_16","alias_value":"HHUYZFKECVXVXNJG","created_at":"2026-06-09T01:05:11.855510+00:00"},{"alias_kind":"pith_short_8","alias_value":"HHUYZFKE","created_at":"2026-06-09T01:05:11.855510+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/HHUYZFKECVXVXNJGD4NC5E5PAC","json":"https://pith.science/pith/HHUYZFKECVXVXNJGD4NC5E5PAC.json","graph_json":"https://pith.science/api/pith-number/HHUYZFKECVXVXNJGD4NC5E5PAC/graph.json","events_json":"https://pith.science/api/pith-number/HHUYZFKECVXVXNJGD4NC5E5PAC/events.json","paper":"https://pith.science/paper/HHUYZFKE"},"agent_actions":{"view_html":"https://pith.science/pith/HHUYZFKECVXVXNJGD4NC5E5PAC","download_json":"https://pith.science/pith/HHUYZFKECVXVXNJGD4NC5E5PAC.json","view_paper":"https://pith.science/paper/HHUYZFKE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2512.15116&json=true","fetch_graph":"https://pith.science/api/pith-number/HHUYZFKECVXVXNJGD4NC5E5PAC/graph.json","fetch_events":"https://pith.science/api/pith-number/HHUYZFKECVXVXNJGD4NC5E5PAC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HHUYZFKECVXVXNJGD4NC5E5PAC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HHUYZFKECVXVXNJGD4NC5E5PAC/action/storage_attestation","attest_author":"https://pith.science/pith/HHUYZFKECVXVXNJGD4NC5E5PAC/action/author_attestation","sign_citation":"https://pith.science/pith/HHUYZFKECVXVXNJGD4NC5E5PAC/action/citation_signature","submit_replication":"https://pith.science/pith/HHUYZFKECVXVXNJGD4NC5E5PAC/action/replication_record"}},"created_at":"2026-06-09T01:05:11.855510+00:00","updated_at":"2026-06-09T01:05:11.855510+00:00"}