{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:RRT2FTVPZQ4QS6IMFGSCPF2PKL","short_pith_number":"pith:RRT2FTVP","canonical_record":{"source":{"id":"2202.03158","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-fin.ST","submitted_at":"2022-01-27T20:32:46Z","cross_cats_sorted":["cs.LG","q-fin.CP","q-fin.TR"],"title_canon_sha256":"5335d5ccf5bda9fc4d51f333c4296778bf4ece40c046afe90bd64f835099a5ea","abstract_canon_sha256":"b819b686e0bde0a034fe6d2c9b2fb832a25ef3821e31c4b9333dcc2085a31bc9"},"schema_version":"1.0"},"canonical_sha256":"8c67a2ceafcc3909790c29a427974f52c00e19c80e86dc196c4fad856f392dd2","source":{"kind":"arxiv","id":"2202.03158","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.03158","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"arxiv_version","alias_value":"2202.03158v1","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.03158","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"pith_short_12","alias_value":"RRT2FTVPZQ4Q","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"pith_short_16","alias_value":"RRT2FTVPZQ4QS6IM","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"pith_short_8","alias_value":"RRT2FTVP","created_at":"2026-07-05T03:55:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:RRT2FTVPZQ4QS6IMFGSCPF2PKL","target":"record","payload":{"canonical_record":{"source":{"id":"2202.03158","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-fin.ST","submitted_at":"2022-01-27T20:32:46Z","cross_cats_sorted":["cs.LG","q-fin.CP","q-fin.TR"],"title_canon_sha256":"5335d5ccf5bda9fc4d51f333c4296778bf4ece40c046afe90bd64f835099a5ea","abstract_canon_sha256":"b819b686e0bde0a034fe6d2c9b2fb832a25ef3821e31c4b9333dcc2085a31bc9"},"schema_version":"1.0"},"canonical_sha256":"8c67a2ceafcc3909790c29a427974f52c00e19c80e86dc196c4fad856f392dd2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:55:20.320744Z","signature_b64":"pNM++T2yrJIXzfiWBbauws6fJbJIPmz8PuWfh64oesYCY6sw8+7cW2NBbUIqH4H91eM0nKn6LiiGtvfljFX/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c67a2ceafcc3909790c29a427974f52c00e19c80e86dc196c4fad856f392dd2","last_reissued_at":"2026-07-05T03:55:20.320330Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:55:20.320330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.03158","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:55:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3TnwHo+nQfncjSnhMPRkoXc4aMsMSbS1u1GS1oF6FAm0Nj2mfk2fwZ4wjVo/4oBP3QRwtYef3MVU6dFLLInsAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:12:06.891979Z"},"content_sha256":"d49e489b91d85dbb06e28262953710fbdf5a7fe722a9cbfe3f6ce7db204a47aa","schema_version":"1.0","event_id":"sha256:d49e489b91d85dbb06e28262953710fbdf5a7fe722a9cbfe3f6ce7db204a47aa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:RRT2FTVPZQ4QS6IMFGSCPF2PKL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","q-fin.CP","q-fin.TR"],"primary_cat":"q-fin.ST","authors_text":"Benyuan Liu, Hongwei Zhu, Jiancheng Shen, Jia Wang, Yu Cao","submitted_at":"2022-01-27T20:32:46Z","abstract_excerpt":"It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a hybrid convolutional LSTM Based variational sequence-to-sequence model with attention (CLVSA), we propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding so"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.03158","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/2202.03158/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:55:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YbuikkbyjJrTyw4QyTnPXzEXe0fJXZUam9uvGQm7k55+kNmD+IeGCol9EhnHCyesQ/eAmxxKYMbriNraeGjvCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:12:06.892376Z"},"content_sha256":"1f7070cd1f65240fe25f9a5ccf3027e3218c000e6d84536fbb37a86873e069b0","schema_version":"1.0","event_id":"sha256:1f7070cd1f65240fe25f9a5ccf3027e3218c000e6d84536fbb37a86873e069b0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RRT2FTVPZQ4QS6IMFGSCPF2PKL/bundle.json","state_url":"https://pith.science/pith/RRT2FTVPZQ4QS6IMFGSCPF2PKL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RRT2FTVPZQ4QS6IMFGSCPF2PKL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T12:12:06Z","links":{"resolver":"https://pith.science/pith/RRT2FTVPZQ4QS6IMFGSCPF2PKL","bundle":"https://pith.science/pith/RRT2FTVPZQ4QS6IMFGSCPF2PKL/bundle.json","state":"https://pith.science/pith/RRT2FTVPZQ4QS6IMFGSCPF2PKL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RRT2FTVPZQ4QS6IMFGSCPF2PKL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:RRT2FTVPZQ4QS6IMFGSCPF2PKL","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":"b819b686e0bde0a034fe6d2c9b2fb832a25ef3821e31c4b9333dcc2085a31bc9","cross_cats_sorted":["cs.LG","q-fin.CP","q-fin.TR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-fin.ST","submitted_at":"2022-01-27T20:32:46Z","title_canon_sha256":"5335d5ccf5bda9fc4d51f333c4296778bf4ece40c046afe90bd64f835099a5ea"},"schema_version":"1.0","source":{"id":"2202.03158","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.03158","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"arxiv_version","alias_value":"2202.03158v1","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.03158","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"pith_short_12","alias_value":"RRT2FTVPZQ4Q","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"pith_short_16","alias_value":"RRT2FTVPZQ4QS6IM","created_at":"2026-07-05T03:55:20Z"},{"alias_kind":"pith_short_8","alias_value":"RRT2FTVP","created_at":"2026-07-05T03:55:20Z"}],"graph_snapshots":[{"event_id":"sha256:1f7070cd1f65240fe25f9a5ccf3027e3218c000e6d84536fbb37a86873e069b0","target":"graph","created_at":"2026-07-05T03:55:20Z","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/2202.03158/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a hybrid convolutional LSTM Based variational sequence-to-sequence model with attention (CLVSA), we propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding so","authors_text":"Benyuan Liu, Hongwei Zhu, Jiancheng Shen, Jia Wang, Yu Cao","cross_cats":["cs.LG","q-fin.CP","q-fin.TR"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-fin.ST","submitted_at":"2022-01-27T20:32:46Z","title":"Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.03158","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:d49e489b91d85dbb06e28262953710fbdf5a7fe722a9cbfe3f6ce7db204a47aa","target":"record","created_at":"2026-07-05T03:55:20Z","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":"b819b686e0bde0a034fe6d2c9b2fb832a25ef3821e31c4b9333dcc2085a31bc9","cross_cats_sorted":["cs.LG","q-fin.CP","q-fin.TR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-fin.ST","submitted_at":"2022-01-27T20:32:46Z","title_canon_sha256":"5335d5ccf5bda9fc4d51f333c4296778bf4ece40c046afe90bd64f835099a5ea"},"schema_version":"1.0","source":{"id":"2202.03158","kind":"arxiv","version":1}},"canonical_sha256":"8c67a2ceafcc3909790c29a427974f52c00e19c80e86dc196c4fad856f392dd2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8c67a2ceafcc3909790c29a427974f52c00e19c80e86dc196c4fad856f392dd2","first_computed_at":"2026-07-05T03:55:20.320330Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:55:20.320330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pNM++T2yrJIXzfiWBbauws6fJbJIPmz8PuWfh64oesYCY6sw8+7cW2NBbUIqH4H91eM0nKn6LiiGtvfljFX/Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:55:20.320744Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.03158","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d49e489b91d85dbb06e28262953710fbdf5a7fe722a9cbfe3f6ce7db204a47aa","sha256:1f7070cd1f65240fe25f9a5ccf3027e3218c000e6d84536fbb37a86873e069b0"],"state_sha256":"e6e68d75929ab1ae59c0d9453a074bd0466bdd168674474187e20dc1c48328af"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aKc5d+y613sa/e1WtnhY1m85gLsMABKwTp5OEYorggrcim2qrC5orj0hI/QIUXLaSdwzxahB0Lef4pWMnDIVAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T12:12:06.894407Z","bundle_sha256":"29a8b9fae2392c14e70d4d204959fc464b3a927504d73d1b93bd96f25d73a8b5"}}