{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VNTXOKJL5US25MWIOTQ67UWMNT","short_pith_number":"pith:VNTXOKJL","schema_version":"1.0","canonical_sha256":"ab6777292bed25aeb2c874e1efd2cc6ccab643439c9cb98987e1c7e8230fa68a","source":{"kind":"arxiv","id":"2410.20679","version":3},"attestation_state":"computed","paper":{"title":"MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-fin.CP"],"primary_cat":"q-fin.ST","authors_text":"Dawei Cheng, Peng Zhu, Qinyuan Liu, Sheng Xiang, Yifan Hu, Yuante Li, Yuqi Liang","submitted_at":"2024-09-25T14:37:49Z","abstract_excerpt":"As financial markets grow increasingly complex in the big data era, accurate stock prediction has become more critical. Traditional time series models, such as GRUs, have been widely used but often struggle to capture the intricate nonlinear dynamics of markets, particularly in the flexible selection and effective utilization of key historical information. Recently, methods like Graph Neural Networks and Reinforcement Learning have shown promise in stock prediction but require high data quality and quantity, and they tend to exhibit instability when dealing with data sparsity and noise. Moreov"},"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":"2410.20679","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2024-09-25T14:37:49Z","cross_cats_sorted":["cs.LG","q-fin.CP"],"title_canon_sha256":"d10c04b558271c3e6f7a60b885cacd7feb5dce633c271d8383b91350c42856f2","abstract_canon_sha256":"510ff8562c17739efe06648d03ca98130ac2bc13f42f864cbec7aa72a3edcb79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:08.445463Z","signature_b64":"b2w0q7/xi5ZUOl0J27tljnaEOgqo7MvSLFyCrPUSTZkSqC+QyPJzb4ybHe3z2pufmahMNGgXzVdEMGYonOKZCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab6777292bed25aeb2c874e1efd2cc6ccab643439c9cb98987e1c7e8230fa68a","last_reissued_at":"2026-07-05T11:59:08.444965Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:08.444965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-fin.CP"],"primary_cat":"q-fin.ST","authors_text":"Dawei Cheng, Peng Zhu, Qinyuan Liu, Sheng Xiang, Yifan Hu, Yuante Li, Yuqi Liang","submitted_at":"2024-09-25T14:37:49Z","abstract_excerpt":"As financial markets grow increasingly complex in the big data era, accurate stock prediction has become more critical. Traditional time series models, such as GRUs, have been widely used but often struggle to capture the intricate nonlinear dynamics of markets, particularly in the flexible selection and effective utilization of key historical information. Recently, methods like Graph Neural Networks and Reinforcement Learning have shown promise in stock prediction but require high data quality and quantity, and they tend to exhibit instability when dealing with data sparsity and noise. Moreov"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.20679","kind":"arxiv","version":3},"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/2410.20679/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":"2410.20679","created_at":"2026-07-05T11:59:08.445021+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.20679v3","created_at":"2026-07-05T11:59:08.445021+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.20679","created_at":"2026-07-05T11:59:08.445021+00:00"},{"alias_kind":"pith_short_12","alias_value":"VNTXOKJL5US2","created_at":"2026-07-05T11:59:08.445021+00:00"},{"alias_kind":"pith_short_16","alias_value":"VNTXOKJL5US25MWI","created_at":"2026-07-05T11:59:08.445021+00:00"},{"alias_kind":"pith_short_8","alias_value":"VNTXOKJL","created_at":"2026-07-05T11:59:08.445021+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.18834","citing_title":"FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting","ref_index":88,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT","json":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT.json","graph_json":"https://pith.science/api/pith-number/VNTXOKJL5US25MWIOTQ67UWMNT/graph.json","events_json":"https://pith.science/api/pith-number/VNTXOKJL5US25MWIOTQ67UWMNT/events.json","paper":"https://pith.science/paper/VNTXOKJL"},"agent_actions":{"view_html":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT","download_json":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT.json","view_paper":"https://pith.science/paper/VNTXOKJL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.20679&json=true","fetch_graph":"https://pith.science/api/pith-number/VNTXOKJL5US25MWIOTQ67UWMNT/graph.json","fetch_events":"https://pith.science/api/pith-number/VNTXOKJL5US25MWIOTQ67UWMNT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT/action/storage_attestation","attest_author":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT/action/author_attestation","sign_citation":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT/action/citation_signature","submit_replication":"https://pith.science/pith/VNTXOKJL5US25MWIOTQ67UWMNT/action/replication_record"}},"created_at":"2026-07-05T11:59:08.445021+00:00","updated_at":"2026-07-05T11:59:08.445021+00:00"}