{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:T43CKFCYXVA2PO3O3MG6EBJ2UV","short_pith_number":"pith:T43CKFCY","schema_version":"1.0","canonical_sha256":"9f36251458bd41a7bb6edb0de2053aa55f4791347b2d64125269b8a517ede56d","source":{"kind":"arxiv","id":"2306.11025","version":1},"attestation_state":"computed","paper":{"title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","q-fin.ST"],"primary_cat":"cs.LG","authors_text":"Shujing Dong, Xinli Yu, Yanbin Lu, Yuan Ling, Zheng Chen, Zongyi Liu","submitted_at":"2023-06-19T15:42:02Z","abstract_excerpt":"This paper presents a novel study on harnessing Large Language Models' (LLMs) outstanding knowledge and reasoning abilities for explainable financial time series forecasting. The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence reasoning and inference, the hurdle of incorporating multi-modal signals from historical news, financial knowledge graphs, etc., and the issue of interpreting and explaining the model results. In this paper, we focus on NASDAQ-100 stocks, making use of publicly accessible historical"},"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":"2306.11025","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-19T15:42:02Z","cross_cats_sorted":["cs.AI","cs.CL","q-fin.ST"],"title_canon_sha256":"5dd20001f23c09b5e95bb43e9e4fead747d1f4112ba80c38dda80aa1e5a8e084","abstract_canon_sha256":"dbad86dd97eab739d81c92cc6bfccdbad01ac592f3f05d376b172e6c68b116c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:22:15.283225Z","signature_b64":"Xzrymjx1BI1MQsyj3YM0542w8rheQKBbtO/izaDOGMoJV1JPqLO/qr11WEZezmxX80spERu/Ihuf4Wmt9mjJDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f36251458bd41a7bb6edb0de2053aa55f4791347b2d64125269b8a517ede56d","last_reissued_at":"2026-07-05T06:22:15.282780Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:22:15.282780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","q-fin.ST"],"primary_cat":"cs.LG","authors_text":"Shujing Dong, Xinli Yu, Yanbin Lu, Yuan Ling, Zheng Chen, Zongyi Liu","submitted_at":"2023-06-19T15:42:02Z","abstract_excerpt":"This paper presents a novel study on harnessing Large Language Models' (LLMs) outstanding knowledge and reasoning abilities for explainable financial time series forecasting. The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence reasoning and inference, the hurdle of incorporating multi-modal signals from historical news, financial knowledge graphs, etc., and the issue of interpreting and explaining the model results. In this paper, we focus on NASDAQ-100 stocks, making use of publicly accessible historical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11025","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/2306.11025/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":"2306.11025","created_at":"2026-07-05T06:22:15.282835+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.11025v1","created_at":"2026-07-05T06:22:15.282835+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11025","created_at":"2026-07-05T06:22:15.282835+00:00"},{"alias_kind":"pith_short_12","alias_value":"T43CKFCYXVA2","created_at":"2026-07-05T06:22:15.282835+00:00"},{"alias_kind":"pith_short_16","alias_value":"T43CKFCYXVA2PO3O","created_at":"2026-07-05T06:22:15.282835+00:00"},{"alias_kind":"pith_short_8","alias_value":"T43CKFCY","created_at":"2026-07-05T06:22:15.282835+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03137","citing_title":"Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03137","citing_title":"Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2509.05215","citing_title":"BEDTime: A Unified Benchmark for Automatically Describing Time Series","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18500","citing_title":"QRAFTI: An Agentic Framework for Empirical Research in Quantitative Finance","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05211","citing_title":"A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06266","citing_title":"Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV","json":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV.json","graph_json":"https://pith.science/api/pith-number/T43CKFCYXVA2PO3O3MG6EBJ2UV/graph.json","events_json":"https://pith.science/api/pith-number/T43CKFCYXVA2PO3O3MG6EBJ2UV/events.json","paper":"https://pith.science/paper/T43CKFCY"},"agent_actions":{"view_html":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV","download_json":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV.json","view_paper":"https://pith.science/paper/T43CKFCY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.11025&json=true","fetch_graph":"https://pith.science/api/pith-number/T43CKFCYXVA2PO3O3MG6EBJ2UV/graph.json","fetch_events":"https://pith.science/api/pith-number/T43CKFCYXVA2PO3O3MG6EBJ2UV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV/action/storage_attestation","attest_author":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV/action/author_attestation","sign_citation":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV/action/citation_signature","submit_replication":"https://pith.science/pith/T43CKFCYXVA2PO3O3MG6EBJ2UV/action/replication_record"}},"created_at":"2026-07-05T06:22:15.282835+00:00","updated_at":"2026-07-05T06:22:15.282835+00:00"}