{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:I3GCFOXA5AKMDYWTLFMINFORRI","short_pith_number":"pith:I3GCFOXA","schema_version":"1.0","canonical_sha256":"46cc22bae0e814c1e2d359588695d18a369f379655a3b75428e34b364ca60275","source":{"kind":"arxiv","id":"2412.03606","version":1},"attestation_state":"computed","paper":{"title":"Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-fin.RM","authors_text":"Kunyuan Ma, Mengfang Sun, Wenqing Zhang, Wenying Sun, You Wu, Zhen Xu","submitted_at":"2024-12-04T08:15:27Z","abstract_excerpt":"This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to complex market changes because they rely on single-dimensional macroeconomic data. This paper proposes a prediction framework based on the Time Series Transformer, which uses the self-attention mechanism of the model to capture the complex temporal dependencies and nonlinear relationships in financial d"},"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":"2412.03606","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2024-12-04T08:15:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d3d79563e577a2f65a949577a0cb4ef287ab8af6e975c4721080f210368f3fed","abstract_canon_sha256":"9652db0634966bab898219ecf0f6865e6216bcc4ba91fe0efc15f112c6dbf274"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:38.898257Z","signature_b64":"5RvK32bYS8DUhHoXqJdGtoG6OXKEVRH4FljBI2LWkPmsJtzsN2wgEjYzwHrgzxCJqmm5EWH4ShsWsAHEbjFECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46cc22bae0e814c1e2d359588695d18a369f379655a3b75428e34b364ca60275","last_reissued_at":"2026-07-05T09:44:38.897783Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:38.897783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-fin.RM","authors_text":"Kunyuan Ma, Mengfang Sun, Wenqing Zhang, Wenying Sun, You Wu, Zhen Xu","submitted_at":"2024-12-04T08:15:27Z","abstract_excerpt":"This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to complex market changes because they rely on single-dimensional macroeconomic data. This paper proposes a prediction framework based on the Time Series Transformer, which uses the self-attention mechanism of the model to capture the complex temporal dependencies and nonlinear relationships in financial d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.03606","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/2412.03606/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":"2412.03606","created_at":"2026-07-05T09:44:38.897841+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.03606v1","created_at":"2026-07-05T09:44:38.897841+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.03606","created_at":"2026-07-05T09:44:38.897841+00:00"},{"alias_kind":"pith_short_12","alias_value":"I3GCFOXA5AKM","created_at":"2026-07-05T09:44:38.897841+00:00"},{"alias_kind":"pith_short_16","alias_value":"I3GCFOXA5AKMDYWT","created_at":"2026-07-05T09:44:38.897841+00:00"},{"alias_kind":"pith_short_8","alias_value":"I3GCFOXA","created_at":"2026-07-05T09:44:38.897841+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/I3GCFOXA5AKMDYWTLFMINFORRI","json":"https://pith.science/pith/I3GCFOXA5AKMDYWTLFMINFORRI.json","graph_json":"https://pith.science/api/pith-number/I3GCFOXA5AKMDYWTLFMINFORRI/graph.json","events_json":"https://pith.science/api/pith-number/I3GCFOXA5AKMDYWTLFMINFORRI/events.json","paper":"https://pith.science/paper/I3GCFOXA"},"agent_actions":{"view_html":"https://pith.science/pith/I3GCFOXA5AKMDYWTLFMINFORRI","download_json":"https://pith.science/pith/I3GCFOXA5AKMDYWTLFMINFORRI.json","view_paper":"https://pith.science/paper/I3GCFOXA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.03606&json=true","fetch_graph":"https://pith.science/api/pith-number/I3GCFOXA5AKMDYWTLFMINFORRI/graph.json","fetch_events":"https://pith.science/api/pith-number/I3GCFOXA5AKMDYWTLFMINFORRI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I3GCFOXA5AKMDYWTLFMINFORRI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I3GCFOXA5AKMDYWTLFMINFORRI/action/storage_attestation","attest_author":"https://pith.science/pith/I3GCFOXA5AKMDYWTLFMINFORRI/action/author_attestation","sign_citation":"https://pith.science/pith/I3GCFOXA5AKMDYWTLFMINFORRI/action/citation_signature","submit_replication":"https://pith.science/pith/I3GCFOXA5AKMDYWTLFMINFORRI/action/replication_record"}},"created_at":"2026-07-05T09:44:38.897841+00:00","updated_at":"2026-07-05T09:44:38.897841+00:00"}