{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:REEOKO4JC4BEMVGFYCAFSGQECW","short_pith_number":"pith:REEOKO4J","schema_version":"1.0","canonical_sha256":"8908e53b8917024654c5c080591a04158bd8a63eb2286cbda7c593275c8d6ea3","source":{"kind":"arxiv","id":"2505.20446","version":1},"attestation_state":"computed","paper":{"title":"Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ilan Naiman, Itai Pemper, Nimrod Berman, Omri Azencot, Tal Gonen","submitted_at":"2025-05-26T18:39:04Z","abstract_excerpt":"Generative modeling of time series is a central challenge in time series analysis, particularly under data-scarce conditions. Despite recent advances in generative modeling, a comprehensive understanding of how state-of-the-art generative models perform under limited supervision remains lacking. In this work, we conduct the first large-scale study evaluating leading generative models in data-scarce settings, revealing a substantial performance gap between full-data and data-scarce regimes. To close this gap, we propose a unified diffusion-based generative framework that can synthesize high-fid"},"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":"2505.20446","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T18:39:04Z","cross_cats_sorted":[],"title_canon_sha256":"7287fe4dd020e43b88f546a1ce0322061e563833a03f767f2daaf727420e4083","abstract_canon_sha256":"6ddb217fe913f7a1294435f100ebb7c51398bb66fd7c12d71fdd02259a89ab97"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:12.559902Z","signature_b64":"m4FQKBwvnrXI/JFHOGrAeKQBzlKnWLkbWswvcVdYGqP0OU5A7AaJILZgHVx+LZB7d44NYhICLUNICMVOJRoUBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8908e53b8917024654c5c080591a04158bd8a63eb2286cbda7c593275c8d6ea3","last_reissued_at":"2026-07-05T11:10:12.559418Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:12.559418Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ilan Naiman, Itai Pemper, Nimrod Berman, Omri Azencot, Tal Gonen","submitted_at":"2025-05-26T18:39:04Z","abstract_excerpt":"Generative modeling of time series is a central challenge in time series analysis, particularly under data-scarce conditions. Despite recent advances in generative modeling, a comprehensive understanding of how state-of-the-art generative models perform under limited supervision remains lacking. In this work, we conduct the first large-scale study evaluating leading generative models in data-scarce settings, revealing a substantial performance gap between full-data and data-scarce regimes. To close this gap, we propose a unified diffusion-based generative framework that can synthesize high-fid"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20446","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/2505.20446/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":"2505.20446","created_at":"2026-07-05T11:10:12.559488+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.20446v1","created_at":"2026-07-05T11:10:12.559488+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20446","created_at":"2026-07-05T11:10:12.559488+00:00"},{"alias_kind":"pith_short_12","alias_value":"REEOKO4JC4BE","created_at":"2026-07-05T11:10:12.559488+00:00"},{"alias_kind":"pith_short_16","alias_value":"REEOKO4JC4BEMVGF","created_at":"2026-07-05T11:10:12.559488+00:00"},{"alias_kind":"pith_short_8","alias_value":"REEOKO4J","created_at":"2026-07-05T11:10:12.559488+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.28867","citing_title":"PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW","json":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW.json","graph_json":"https://pith.science/api/pith-number/REEOKO4JC4BEMVGFYCAFSGQECW/graph.json","events_json":"https://pith.science/api/pith-number/REEOKO4JC4BEMVGFYCAFSGQECW/events.json","paper":"https://pith.science/paper/REEOKO4J"},"agent_actions":{"view_html":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW","download_json":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW.json","view_paper":"https://pith.science/paper/REEOKO4J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.20446&json=true","fetch_graph":"https://pith.science/api/pith-number/REEOKO4JC4BEMVGFYCAFSGQECW/graph.json","fetch_events":"https://pith.science/api/pith-number/REEOKO4JC4BEMVGFYCAFSGQECW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW/action/storage_attestation","attest_author":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW/action/author_attestation","sign_citation":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW/action/citation_signature","submit_replication":"https://pith.science/pith/REEOKO4JC4BEMVGFYCAFSGQECW/action/replication_record"}},"created_at":"2026-07-05T11:10:12.559488+00:00","updated_at":"2026-07-05T11:10:12.559488+00:00"}