{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SJOW73W2HEFHSTSYFYVD2ADPI7","short_pith_number":"pith:SJOW73W2","schema_version":"1.0","canonical_sha256":"925d6feeda390a794e582e2a3d006f47d325035c97c7a8b2802dfdf6756af4cc","source":{"kind":"arxiv","id":"2502.11418","version":2},"attestation_state":"computed","paper":{"title":"TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Geon Lee, Haifeng Chen, Kijung Shin, Wei Cheng, Wenchao Yu","submitted_at":"2025-02-17T04:17:27Z","abstract_excerpt":"Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associated with real-world time series data is often essential for accurate and reliable event predictions. In this paper, we introduce TimeCAP, a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data, extending their typical usage as predictors. TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the "},"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":"2502.11418","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-17T04:17:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"aaf3fd1ccc788ead7c6a97d76392ba174fac607572df4b48b0efae3dd7d1a903","abstract_canon_sha256":"d5f931e34e76afe8fe4c9f6e682f5b0f09da1c8256b12182ddddc1675dc9a38e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:27:25.266668Z","signature_b64":"gi1ldWhFxJBvHxDelFTPsgAG/ryWLnBNBkJp8vrEU/T5wiYmarNP0a8p/2M08MrACmgmgYJ10chg0WyLimqZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"925d6feeda390a794e582e2a3d006f47d325035c97c7a8b2802dfdf6756af4cc","last_reissued_at":"2026-07-05T10:27:25.266165Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:27:25.266165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Geon Lee, Haifeng Chen, Kijung Shin, Wei Cheng, Wenchao Yu","submitted_at":"2025-02-17T04:17:27Z","abstract_excerpt":"Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associated with real-world time series data is often essential for accurate and reliable event predictions. In this paper, we introduce TimeCAP, a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data, extending their typical usage as predictors. TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11418","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/2502.11418/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":"2502.11418","created_at":"2026-07-05T10:27:25.266222+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.11418v2","created_at":"2026-07-05T10:27:25.266222+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11418","created_at":"2026-07-05T10:27:25.266222+00:00"},{"alias_kind":"pith_short_12","alias_value":"SJOW73W2HEFH","created_at":"2026-07-05T10:27:25.266222+00:00"},{"alias_kind":"pith_short_16","alias_value":"SJOW73W2HEFHSTSY","created_at":"2026-07-05T10:27:25.266222+00:00"},{"alias_kind":"pith_short_8","alias_value":"SJOW73W2","created_at":"2026-07-05T10:27:25.266222+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.11512","citing_title":"From Time Series Analysis to Question Answering: A Survey in the LLM Era","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19633","citing_title":"Time Series Augmented Generation for Financial Applications","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7","json":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7.json","graph_json":"https://pith.science/api/pith-number/SJOW73W2HEFHSTSYFYVD2ADPI7/graph.json","events_json":"https://pith.science/api/pith-number/SJOW73W2HEFHSTSYFYVD2ADPI7/events.json","paper":"https://pith.science/paper/SJOW73W2"},"agent_actions":{"view_html":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7","download_json":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7.json","view_paper":"https://pith.science/paper/SJOW73W2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.11418&json=true","fetch_graph":"https://pith.science/api/pith-number/SJOW73W2HEFHSTSYFYVD2ADPI7/graph.json","fetch_events":"https://pith.science/api/pith-number/SJOW73W2HEFHSTSYFYVD2ADPI7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7/action/storage_attestation","attest_author":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7/action/author_attestation","sign_citation":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7/action/citation_signature","submit_replication":"https://pith.science/pith/SJOW73W2HEFHSTSYFYVD2ADPI7/action/replication_record"}},"created_at":"2026-07-05T10:27:25.266222+00:00","updated_at":"2026-07-05T10:27:25.266222+00:00"}