{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TWWZQBO5S2CWH4BNPMEWUTRKVL","short_pith_number":"pith:TWWZQBO5","schema_version":"1.0","canonical_sha256":"9dad9805dd968563f02d7b096a4e2aaac6360f00b9c3e24797e87205abb66053","source":{"kind":"arxiv","id":"2211.11513","version":1},"attestation_state":"computed","paper":{"title":"DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"q-fin.ST","authors_text":"Defu Cao, Loc Trinh, Svitlana Vyetrenko, Yan Liu, Yousef El-Laham","submitted_at":"2022-11-17T06:33:27Z","abstract_excerpt":"In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since distributional shifts are unlabeled in current publicly available LOB datasets. Therefore, it is critical to build a synthetic LOB dataset with labeled OOD samples serving as a testbed for developing models that generalize well to unseen s"},"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":"2211.11513","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2022-11-17T06:33:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"6b64d9143e42c457e3faf58e82dda663b7c3cf7621fb054ca04be365d0d3669f","abstract_canon_sha256":"4c3fa601077e8711ee6a40af63776866a3024c7ea6ab4cfdd1fbd776d4948ebb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:17:54.431200Z","signature_b64":"c+3KZkKRjak9VBQuTjaIJoCj7nPStVm14VP7Xl9rV7JyqUl7QUoAhCs98Bnb5MydG6yuQgnU5d1mDSl2S9Y4AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9dad9805dd968563f02d7b096a4e2aaac6360f00b9c3e24797e87205abb66053","last_reissued_at":"2026-07-05T05:17:54.430781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:17:54.430781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"q-fin.ST","authors_text":"Defu Cao, Loc Trinh, Svitlana Vyetrenko, Yan Liu, Yousef El-Laham","submitted_at":"2022-11-17T06:33:27Z","abstract_excerpt":"In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since distributional shifts are unlabeled in current publicly available LOB datasets. Therefore, it is critical to build a synthetic LOB dataset with labeled OOD samples serving as a testbed for developing models that generalize well to unseen s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.11513","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/2211.11513/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":"2211.11513","created_at":"2026-07-05T05:17:54.430837+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.11513v1","created_at":"2026-07-05T05:17:54.430837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.11513","created_at":"2026-07-05T05:17:54.430837+00:00"},{"alias_kind":"pith_short_12","alias_value":"TWWZQBO5S2CW","created_at":"2026-07-05T05:17:54.430837+00:00"},{"alias_kind":"pith_short_16","alias_value":"TWWZQBO5S2CWH4BN","created_at":"2026-07-05T05:17:54.430837+00:00"},{"alias_kind":"pith_short_8","alias_value":"TWWZQBO5","created_at":"2026-07-05T05:17:54.430837+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.09172","citing_title":"LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data","ref_index":2020,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL","json":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL.json","graph_json":"https://pith.science/api/pith-number/TWWZQBO5S2CWH4BNPMEWUTRKVL/graph.json","events_json":"https://pith.science/api/pith-number/TWWZQBO5S2CWH4BNPMEWUTRKVL/events.json","paper":"https://pith.science/paper/TWWZQBO5"},"agent_actions":{"view_html":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL","download_json":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL.json","view_paper":"https://pith.science/paper/TWWZQBO5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.11513&json=true","fetch_graph":"https://pith.science/api/pith-number/TWWZQBO5S2CWH4BNPMEWUTRKVL/graph.json","fetch_events":"https://pith.science/api/pith-number/TWWZQBO5S2CWH4BNPMEWUTRKVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL/action/storage_attestation","attest_author":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL/action/author_attestation","sign_citation":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL/action/citation_signature","submit_replication":"https://pith.science/pith/TWWZQBO5S2CWH4BNPMEWUTRKVL/action/replication_record"}},"created_at":"2026-07-05T05:17:54.430837+00:00","updated_at":"2026-07-05T05:17:54.430837+00:00"}