{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JEZEU6VKJQJ4V52GSLLRKPKPSK","short_pith_number":"pith:JEZEU6VK","schema_version":"1.0","canonical_sha256":"49324a7aaa4c13caf74692d7153d4f92bc5d30bf99a8cf167e2ca1798b9d0612","source":{"kind":"arxiv","id":"2508.19609","version":1},"attestation_state":"computed","paper":{"title":"FinCast: A Foundation Model for Financial Time-Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-fin.CP"],"primary_cat":"cs.LG","authors_text":"Haodong Chen, Qiang Qu, Vera Chung, Zhuohang Zhu","submitted_at":"2025-08-27T06:44:46Z","abstract_excerpt":"Financial time-series forecasting is critical for maintaining economic stability, guiding informed policymaking, and promoting sustainable investment practices. However, it remains challenging due to various underlying pattern shifts. These shifts arise primarily from three sources: temporal non-stationarity (distribution changes over time), multi-domain diversity (distinct patterns across financial domains such as stocks, commodities, and futures), and varying temporal resolutions (patterns differing across per-second, hourly, daily, or weekly indicators). While recent deep learning methods a"},"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":"2508.19609","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T06:44:46Z","cross_cats_sorted":["cs.AI","q-fin.CP"],"title_canon_sha256":"e9ae1534ddfff6ba7e8bcbc7203b72c201e4416822341344919b2bc146c2c92e","abstract_canon_sha256":"ce0062f2398e00547bca887f6ed1f4d7130325cdbce08203b7dc9f5fef483fb2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:14.990211Z","signature_b64":"K97V3+0YaFO1hQGt4nOhCVLtKg6p2QZTrQGd+5Hw6wbg4WCgMOOEcPxmAFlycoi0+nnTyOSmgEegZHTukLxqCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49324a7aaa4c13caf74692d7153d4f92bc5d30bf99a8cf167e2ca1798b9d0612","last_reissued_at":"2026-07-05T12:00:14.989729Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:14.989729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FinCast: A Foundation Model for Financial Time-Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-fin.CP"],"primary_cat":"cs.LG","authors_text":"Haodong Chen, Qiang Qu, Vera Chung, Zhuohang Zhu","submitted_at":"2025-08-27T06:44:46Z","abstract_excerpt":"Financial time-series forecasting is critical for maintaining economic stability, guiding informed policymaking, and promoting sustainable investment practices. However, it remains challenging due to various underlying pattern shifts. These shifts arise primarily from three sources: temporal non-stationarity (distribution changes over time), multi-domain diversity (distinct patterns across financial domains such as stocks, commodities, and futures), and varying temporal resolutions (patterns differing across per-second, hourly, daily, or weekly indicators). While recent deep learning methods a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19609","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/2508.19609/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":"2508.19609","created_at":"2026-07-05T12:00:14.989791+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.19609v1","created_at":"2026-07-05T12:00:14.989791+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19609","created_at":"2026-07-05T12:00:14.989791+00:00"},{"alias_kind":"pith_short_12","alias_value":"JEZEU6VKJQJ4","created_at":"2026-07-05T12:00:14.989791+00:00"},{"alias_kind":"pith_short_16","alias_value":"JEZEU6VKJQJ4V52G","created_at":"2026-07-05T12:00:14.989791+00:00"},{"alias_kind":"pith_short_8","alias_value":"JEZEU6VK","created_at":"2026-07-05T12:00:14.989791+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05291","citing_title":"Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks","ref_index":91,"is_internal_anchor":true},{"citing_arxiv_id":"2606.09643","citing_title":"FMplex: Model Virtualization for Serving Extensible Foundation Models","ref_index":86,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK","json":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK.json","graph_json":"https://pith.science/api/pith-number/JEZEU6VKJQJ4V52GSLLRKPKPSK/graph.json","events_json":"https://pith.science/api/pith-number/JEZEU6VKJQJ4V52GSLLRKPKPSK/events.json","paper":"https://pith.science/paper/JEZEU6VK"},"agent_actions":{"view_html":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK","download_json":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK.json","view_paper":"https://pith.science/paper/JEZEU6VK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.19609&json=true","fetch_graph":"https://pith.science/api/pith-number/JEZEU6VKJQJ4V52GSLLRKPKPSK/graph.json","fetch_events":"https://pith.science/api/pith-number/JEZEU6VKJQJ4V52GSLLRKPKPSK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK/action/storage_attestation","attest_author":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK/action/author_attestation","sign_citation":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK/action/citation_signature","submit_replication":"https://pith.science/pith/JEZEU6VKJQJ4V52GSLLRKPKPSK/action/replication_record"}},"created_at":"2026-07-05T12:00:14.989791+00:00","updated_at":"2026-07-05T12:00:14.989791+00:00"}