{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:WBDGWJ4RBDMKRVMNP2SNGRMM3T","short_pith_number":"pith:WBDGWJ4R","canonical_record":{"source":{"id":"2105.01402","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-04T10:27:37Z","cross_cats_sorted":[],"title_canon_sha256":"4e43f3731a2757285e989774d83037d6c7928c229568d83f22c73d2e9a73f501","abstract_canon_sha256":"1b8316d8e6a0dfb6aac1b522d84e79c0e160a8ffbaaa0b3342477befefa4ac2c"},"schema_version":"1.0"},"canonical_sha256":"b0466b279108d8a8d58d7ea4d3458cdcfc60ed4b30c6adb16e0ce9855c4871a6","source":{"kind":"arxiv","id":"2105.01402","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.01402","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"arxiv_version","alias_value":"2105.01402v1","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.01402","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"pith_short_12","alias_value":"WBDGWJ4RBDMK","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"pith_short_16","alias_value":"WBDGWJ4RBDMKRVMN","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"pith_short_8","alias_value":"WBDGWJ4R","created_at":"2026-07-05T02:37:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:WBDGWJ4RBDMKRVMNP2SNGRMM3T","target":"record","payload":{"canonical_record":{"source":{"id":"2105.01402","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-04T10:27:37Z","cross_cats_sorted":[],"title_canon_sha256":"4e43f3731a2757285e989774d83037d6c7928c229568d83f22c73d2e9a73f501","abstract_canon_sha256":"1b8316d8e6a0dfb6aac1b522d84e79c0e160a8ffbaaa0b3342477befefa4ac2c"},"schema_version":"1.0"},"canonical_sha256":"b0466b279108d8a8d58d7ea4d3458cdcfc60ed4b30c6adb16e0ce9855c4871a6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:37:16.831374Z","signature_b64":"L0gAWiXPG9CJv17zLfm0fVs4oxWMOprIiOFTbUftXE2nYgAOaPEnuuCzVtH7WVNpjBQGWxKkoMn8lIwliiZgCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0466b279108d8a8d58d7ea4d3458cdcfc60ed4b30c6adb16e0ce9855c4871a6","last_reissued_at":"2026-07-05T02:37:16.830958Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:37:16.830958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.01402","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":"2026-07-05T02:37:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AY8aoEaLX6ISVrDA7pqaRqtECFt1TUFVFEWiMlXl8MAXOJcTiNUHDSNI0lVCjCpFEn3W3bybaihbrJfE9PhJAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T14:28:06.219854Z"},"content_sha256":"c3c9ce7e140a884db25dcf24aa379be08645781bd8285a09cff5c726033d43cc","schema_version":"1.0","event_id":"sha256:c3c9ce7e140a884db25dcf24aa379be08645781bd8285a09cff5c726033d43cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:WBDGWJ4RBDMKRVMNP2SNGRMM3T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Using Twitter Attribute Information to Predict Stock Prices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ebba Leckstr\\\"om, Roderick Karlemstrand","submitted_at":"2021-05-04T10:27:37Z","abstract_excerpt":"Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media. With the help of Machine Learning, complex patterns in data can be identified beyond the human intellect. In this thesis, a Machine Learning model for time series forecasting is created and tested to predict stock prices. The model is based on a neural network with several layers of LSTM and fully connected layers. It is trained with historical stock values"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.01402","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/2105.01402/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":"2026-07-05T02:37:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W885ea+wyuw7ZvJ/fcSxBc8RNVTpBVzwhmO8wXluhJVMs3E0I1zP9LhMe+GdUFSzoGJn/Tlc79reKni01QBhDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T14:28:06.220410Z"},"content_sha256":"a94e697041860763e5907b797153cd53478ede523dc4255cffb0ad4d6e394a0b","schema_version":"1.0","event_id":"sha256:a94e697041860763e5907b797153cd53478ede523dc4255cffb0ad4d6e394a0b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WBDGWJ4RBDMKRVMNP2SNGRMM3T/bundle.json","state_url":"https://pith.science/pith/WBDGWJ4RBDMKRVMNP2SNGRMM3T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WBDGWJ4RBDMKRVMNP2SNGRMM3T/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":"2026-08-09T14:28:06Z","links":{"resolver":"https://pith.science/pith/WBDGWJ4RBDMKRVMNP2SNGRMM3T","bundle":"https://pith.science/pith/WBDGWJ4RBDMKRVMNP2SNGRMM3T/bundle.json","state":"https://pith.science/pith/WBDGWJ4RBDMKRVMNP2SNGRMM3T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WBDGWJ4RBDMKRVMNP2SNGRMM3T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:WBDGWJ4RBDMKRVMNP2SNGRMM3T","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":"1b8316d8e6a0dfb6aac1b522d84e79c0e160a8ffbaaa0b3342477befefa4ac2c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-04T10:27:37Z","title_canon_sha256":"4e43f3731a2757285e989774d83037d6c7928c229568d83f22c73d2e9a73f501"},"schema_version":"1.0","source":{"id":"2105.01402","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.01402","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"arxiv_version","alias_value":"2105.01402v1","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.01402","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"pith_short_12","alias_value":"WBDGWJ4RBDMK","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"pith_short_16","alias_value":"WBDGWJ4RBDMKRVMN","created_at":"2026-07-05T02:37:16Z"},{"alias_kind":"pith_short_8","alias_value":"WBDGWJ4R","created_at":"2026-07-05T02:37:16Z"}],"graph_snapshots":[{"event_id":"sha256:a94e697041860763e5907b797153cd53478ede523dc4255cffb0ad4d6e394a0b","target":"graph","created_at":"2026-07-05T02:37:16Z","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/2105.01402/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media. With the help of Machine Learning, complex patterns in data can be identified beyond the human intellect. In this thesis, a Machine Learning model for time series forecasting is created and tested to predict stock prices. The model is based on a neural network with several layers of LSTM and fully connected layers. It is trained with historical stock values","authors_text":"Ebba Leckstr\\\"om, Roderick Karlemstrand","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-04T10:27:37Z","title":"Using Twitter Attribute Information to Predict Stock Prices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.01402","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:c3c9ce7e140a884db25dcf24aa379be08645781bd8285a09cff5c726033d43cc","target":"record","created_at":"2026-07-05T02:37:16Z","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":"1b8316d8e6a0dfb6aac1b522d84e79c0e160a8ffbaaa0b3342477befefa4ac2c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-04T10:27:37Z","title_canon_sha256":"4e43f3731a2757285e989774d83037d6c7928c229568d83f22c73d2e9a73f501"},"schema_version":"1.0","source":{"id":"2105.01402","kind":"arxiv","version":1}},"canonical_sha256":"b0466b279108d8a8d58d7ea4d3458cdcfc60ed4b30c6adb16e0ce9855c4871a6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b0466b279108d8a8d58d7ea4d3458cdcfc60ed4b30c6adb16e0ce9855c4871a6","first_computed_at":"2026-07-05T02:37:16.830958Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:37:16.830958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L0gAWiXPG9CJv17zLfm0fVs4oxWMOprIiOFTbUftXE2nYgAOaPEnuuCzVtH7WVNpjBQGWxKkoMn8lIwliiZgCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:37:16.831374Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.01402","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3c9ce7e140a884db25dcf24aa379be08645781bd8285a09cff5c726033d43cc","sha256:a94e697041860763e5907b797153cd53478ede523dc4255cffb0ad4d6e394a0b"],"state_sha256":"8e329cf88b069d40c986ff3b383ca3c7bd842b8d43e81a3dbbb93f69dcd4638b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zCUnjW39IwH6D5BGopg/j+B4nnhoOU6WRnLJHkdC8brN4sn7E98LGCXOWHh6rH+7QhQX3vcJbsmWUUyPbtO/Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T14:28:06.224004Z","bundle_sha256":"82ecd7469d3b401412642e748dc189e064808e32f46e694b86afb9162e4b6e01"}}