{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VKAHWOOGL4ECZJRJ522YCNL6KL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4107fef9ebad7be53dc17f154abd31bd96a27d78f9d2e6fa2988b224a7d7855a","cross_cats_sorted":["q-fin.ST"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2017-11-11T17:33:42Z","title_canon_sha256":"c035664118387b7b710a54c024a409129c0af10ba4827927694b25066e33aa27"},"schema_version":"1.0","source":{"id":"1711.04174","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.04174","created_at":"2026-05-18T00:30:39Z"},{"alias_kind":"arxiv_version","alias_value":"1711.04174v1","created_at":"2026-05-18T00:30:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.04174","created_at":"2026-05-18T00:30:39Z"},{"alias_kind":"pith_short_12","alias_value":"VKAHWOOGL4EC","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VKAHWOOGL4ECZJRJ","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VKAHWOOG","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:30667c3f845eae80a33005f8a0af053255720a531dc448f2d49767ccda81f5e2","target":"graph","created_at":"2026-05-18T00:30:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In this work we present a data-driven end-to-end Deep Learning approach for time series prediction, applied to financial time series. A Deep Learning scheme is derived to predict the temporal trends of stocks and ETFs in NYSE or NASDAQ. Our approach is based on a neural network (NN) that is applied to raw financial data inputs, and is trained to predict the temporal trends of stocks and ETFs. In order to handle commission-based trading, we derive an investment strategy that utilizes the probabilistic outputs of the NN, and optimizes the average return. The proposed scheme is shown to provide s","authors_text":"Ariel Navon, Yosi Keller","cross_cats":["q-fin.ST"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2017-11-11T17:33:42Z","title":"Financial Time Series Prediction Using Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.04174","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d3830bb6f9adfe2c2d4cb9d1b684d0b442baca31c73ae72672683d4d69df99a4","target":"record","created_at":"2026-05-18T00:30:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4107fef9ebad7be53dc17f154abd31bd96a27d78f9d2e6fa2988b224a7d7855a","cross_cats_sorted":["q-fin.ST"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2017-11-11T17:33:42Z","title_canon_sha256":"c035664118387b7b710a54c024a409129c0af10ba4827927694b25066e33aa27"},"schema_version":"1.0","source":{"id":"1711.04174","kind":"arxiv","version":1}},"canonical_sha256":"aa807b39c65f082ca629eeb581357e52c2d6d801b11f694538e5c80f354e8344","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aa807b39c65f082ca629eeb581357e52c2d6d801b11f694538e5c80f354e8344","first_computed_at":"2026-05-18T00:30:39.889831Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:30:39.889831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/okWEXh+lGqTbws9Fv6RfqAczuA5pY0cIpcqCd3TDS06f++KnkDPOzcV6Z04wfmrwGsK5Tb93bVF2LU7YU45Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T00:30:39.890504Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.04174","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d3830bb6f9adfe2c2d4cb9d1b684d0b442baca31c73ae72672683d4d69df99a4","sha256:30667c3f845eae80a33005f8a0af053255720a531dc448f2d49767ccda81f5e2"],"state_sha256":"7cf02b4cfeebeaf9d172236baa7bfedeab0f0386679deba0c37c0928e74cda87"}