{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QQNBEOD6MYVWRG5VFSY7HJEQSB","short_pith_number":"pith:QQNBEOD6","schema_version":"1.0","canonical_sha256":"841a12387e662b689bb52cb1f3a490904158cc592e3205c8c41aa9297cee440e","source":{"kind":"arxiv","id":"2412.20810","version":1},"attestation_state":"computed","paper":{"title":"TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chang Xu, Huanyu Zhang, Jiang Bian, Liang Wang, Tieniu Tan, Yi-Fan Zhang, Zhang Zhang","submitted_at":"2024-12-30T09:06:47Z","abstract_excerpt":"Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable generalization capabilities, such as zero-shot learning, through large-scale pre-training. Meanwhile, Retrieval-Augmented Generation (RAG) methods have been widely employed to enhance the performance of foundation models on unseen data, allowing models to access to external knowledge. In this paper, we introduce TimeRAF, a Retrieval-Augmented Forecasting model that enhance zero-sho"},"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":"2412.20810","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-30T09:06:47Z","cross_cats_sorted":[],"title_canon_sha256":"d2566e9d6baae380566d0021752121506034abbde5ab756da97b38f85c9a8513","abstract_canon_sha256":"93db15b3e1e624e579dda486d48ff957476ed49dcaa44dc7a52acb1e79e1ff77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:22.728492Z","signature_b64":"plfPN+nvRTze7JBSjC7x9GvmGuGRqmDLMdHya80zKRfYGfF5X/5PZppUuWxUSzPVDnyNnEjSIvLzHQz53yeRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"841a12387e662b689bb52cb1f3a490904158cc592e3205c8c41aa9297cee440e","last_reissued_at":"2026-07-05T09:55:22.727888Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:22.727888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chang Xu, Huanyu Zhang, Jiang Bian, Liang Wang, Tieniu Tan, Yi-Fan Zhang, Zhang Zhang","submitted_at":"2024-12-30T09:06:47Z","abstract_excerpt":"Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable generalization capabilities, such as zero-shot learning, through large-scale pre-training. Meanwhile, Retrieval-Augmented Generation (RAG) methods have been widely employed to enhance the performance of foundation models on unseen data, allowing models to access to external knowledge. In this paper, we introduce TimeRAF, a Retrieval-Augmented Forecasting model that enhance zero-sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20810","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/2412.20810/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":"2412.20810","created_at":"2026-07-05T09:55:22.727958+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.20810v1","created_at":"2026-07-05T09:55:22.727958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20810","created_at":"2026-07-05T09:55:22.727958+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQNBEOD6MYVW","created_at":"2026-07-05T09:55:22.727958+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQNBEOD6MYVWRG5V","created_at":"2026-07-05T09:55:22.727958+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQNBEOD6","created_at":"2026-07-05T09:55:22.727958+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.11040","citing_title":"Large Language models for Time Series Analysis: Techniques, Applications, and Challenges","ref_index":54,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB","json":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB.json","graph_json":"https://pith.science/api/pith-number/QQNBEOD6MYVWRG5VFSY7HJEQSB/graph.json","events_json":"https://pith.science/api/pith-number/QQNBEOD6MYVWRG5VFSY7HJEQSB/events.json","paper":"https://pith.science/paper/QQNBEOD6"},"agent_actions":{"view_html":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB","download_json":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB.json","view_paper":"https://pith.science/paper/QQNBEOD6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.20810&json=true","fetch_graph":"https://pith.science/api/pith-number/QQNBEOD6MYVWRG5VFSY7HJEQSB/graph.json","fetch_events":"https://pith.science/api/pith-number/QQNBEOD6MYVWRG5VFSY7HJEQSB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB/action/storage_attestation","attest_author":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB/action/author_attestation","sign_citation":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB/action/citation_signature","submit_replication":"https://pith.science/pith/QQNBEOD6MYVWRG5VFSY7HJEQSB/action/replication_record"}},"created_at":"2026-07-05T09:55:22.727958+00:00","updated_at":"2026-07-05T09:55:22.727958+00:00"}