{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7L6CYR3XQIJXEVFTW2ZTBTT2WR","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":"678b9f2c700e0ccd409eeca863e46136d3bfb35293ac657f1c7c0660e18dff69","cross_cats_sorted":["cs.NE","q-fin.TR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-21T16:06:41Z","title_canon_sha256":"ffb85110ff80297758f498994d9d9e586e7722191488cadf3f77b198044425a6"},"schema_version":"1.0","source":{"id":"2105.10430","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.10430","created_at":"2026-07-05T03:09:18Z"},{"alias_kind":"arxiv_version","alias_value":"2105.10430v2","created_at":"2026-07-05T03:09:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.10430","created_at":"2026-07-05T03:09:18Z"},{"alias_kind":"pith_short_12","alias_value":"7L6CYR3XQIJX","created_at":"2026-07-05T03:09:18Z"},{"alias_kind":"pith_short_16","alias_value":"7L6CYR3XQIJXEVFT","created_at":"2026-07-05T03:09:18Z"},{"alias_kind":"pith_short_8","alias_value":"7L6CYR3X","created_at":"2026-07-05T03:09:18Z"}],"graph_snapshots":[{"event_id":"sha256:5a3f5d9dbe2374c0f6e92d637e122b46771f37b9f192d842cd07739f56166455","target":"graph","created_at":"2026-07-05T03:09:18Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2105.10430/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We design multi-horizon forecasting models for limit order book (LOB) data by using deep learning techniques. Unlike standard structures where a single prediction is made, we adopt encoder-decoder models with sequence-to-sequence and Attention mechanisms to generate a forecasting path. Our methods achieve comparable performance to state-of-art algorithms at short prediction horizons. Importantly, they outperform when generating predictions over long horizons by leveraging the multi-horizon setup. Given that encoder-decoder models rely on recurrent neural layers, they generally suffer from slow","authors_text":"Stefan Zohren, Zihao Zhang","cross_cats":["cs.NE","q-fin.TR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-21T16:06:41Z","title":"Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.10430","kind":"arxiv","version":2},"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":"sha256:43398d7862b312565df14877c032e8162c70b2f6a3c16f6e4d2d41601c195063","target":"record","created_at":"2026-07-05T03:09:18Z","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":"678b9f2c700e0ccd409eeca863e46136d3bfb35293ac657f1c7c0660e18dff69","cross_cats_sorted":["cs.NE","q-fin.TR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-21T16:06:41Z","title_canon_sha256":"ffb85110ff80297758f498994d9d9e586e7722191488cadf3f77b198044425a6"},"schema_version":"1.0","source":{"id":"2105.10430","kind":"arxiv","version":2}},"canonical_sha256":"fafc2c477782137254b3b6b330ce7ab4520cb560cfc6a5d96a0b02f4176e276a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fafc2c477782137254b3b6b330ce7ab4520cb560cfc6a5d96a0b02f4176e276a","first_computed_at":"2026-07-05T03:09:18.611310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:09:18.611310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"luC3/jHlrRPvuE6TfZgyvsaUu4T7+IqTwN2O3jFa3ut5RqKFel7xfPTZSGkMWO9cLu9TgQ8k8h3xG+eaRJK6CA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:09:18.611731Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.10430","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43398d7862b312565df14877c032e8162c70b2f6a3c16f6e4d2d41601c195063","sha256:5a3f5d9dbe2374c0f6e92d637e122b46771f37b9f192d842cd07739f56166455"],"state_sha256":"f7e02b707ce4e3a913e220b96c7a302e442e504b02087321ab56150247451f5c"}