{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5GIHNNGVFABD5QQODTXDHAVNQ5","short_pith_number":"pith:5GIHNNGV","schema_version":"1.0","canonical_sha256":"e99076b4d528023ec20e1cee3382ad8776729422d256a29bfe19fc9d5546883a","source":{"kind":"arxiv","id":"2410.19241","version":2},"attestation_state":"computed","paper":{"title":"Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andi Chen, Chihang Wang, Fangyu Wu, Haowei Ni, Mengyao Zheng, Panfeng Li, Shuchen Meng, Xupeng Chen","submitted_at":"2024-10-25T01:29:54Z","abstract_excerpt":"Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic and financial research. Traditional forecasting models often falter when addressing the inherent complexities and non-linearities of exchange rate data. This study explores the application of advanced deep learning models, including LSTM, CNN, and transformer-based architectures, to enhance the predictive accuracy of the RMB/USD exchange rate. Utilizing 40 features across 6 categories, the analysis identifies TSMixer as the most effective model for thi"},"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":"2410.19241","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-25T01:29:54Z","cross_cats_sorted":[],"title_canon_sha256":"d1a33a643a7f9d72591c0ad730ac891c93f183bc82990517c277bb5c9280f3ab","abstract_canon_sha256":"f8d76bce3e1d16cb66017b6f73a3f9065b90da0e1eef93ad25d0847006c9bf50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:05.294275Z","signature_b64":"NpVeEGfv9KH2qQC50bEyVHuaPf9IbrdgpAeQ5uTsU4nwuOUJEzeuv9a+vMG0jAlLN27aawLjzpCIGmsk9ebaCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e99076b4d528023ec20e1cee3382ad8776729422d256a29bfe19fc9d5546883a","last_reissued_at":"2026-07-05T09:54:05.293765Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:05.293765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andi Chen, Chihang Wang, Fangyu Wu, Haowei Ni, Mengyao Zheng, Panfeng Li, Shuchen Meng, Xupeng Chen","submitted_at":"2024-10-25T01:29:54Z","abstract_excerpt":"Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic and financial research. Traditional forecasting models often falter when addressing the inherent complexities and non-linearities of exchange rate data. This study explores the application of advanced deep learning models, including LSTM, CNN, and transformer-based architectures, to enhance the predictive accuracy of the RMB/USD exchange rate. Utilizing 40 features across 6 categories, the analysis identifies TSMixer as the most effective model for thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.19241","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/2410.19241/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":"2410.19241","created_at":"2026-07-05T09:54:05.293826+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.19241v2","created_at":"2026-07-05T09:54:05.293826+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.19241","created_at":"2026-07-05T09:54:05.293826+00:00"},{"alias_kind":"pith_short_12","alias_value":"5GIHNNGVFABD","created_at":"2026-07-05T09:54:05.293826+00:00"},{"alias_kind":"pith_short_16","alias_value":"5GIHNNGVFABD5QQO","created_at":"2026-07-05T09:54:05.293826+00:00"},{"alias_kind":"pith_short_8","alias_value":"5GIHNNGV","created_at":"2026-07-05T09:54:05.293826+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5","json":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5.json","graph_json":"https://pith.science/api/pith-number/5GIHNNGVFABD5QQODTXDHAVNQ5/graph.json","events_json":"https://pith.science/api/pith-number/5GIHNNGVFABD5QQODTXDHAVNQ5/events.json","paper":"https://pith.science/paper/5GIHNNGV"},"agent_actions":{"view_html":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5","download_json":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5.json","view_paper":"https://pith.science/paper/5GIHNNGV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.19241&json=true","fetch_graph":"https://pith.science/api/pith-number/5GIHNNGVFABD5QQODTXDHAVNQ5/graph.json","fetch_events":"https://pith.science/api/pith-number/5GIHNNGVFABD5QQODTXDHAVNQ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5/action/storage_attestation","attest_author":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5/action/author_attestation","sign_citation":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5/action/citation_signature","submit_replication":"https://pith.science/pith/5GIHNNGVFABD5QQODTXDHAVNQ5/action/replication_record"}},"created_at":"2026-07-05T09:54:05.293826+00:00","updated_at":"2026-07-05T09:54:05.293826+00:00"}