{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:6FFU767AXLDGBMGDJ2VQIL3OOE","short_pith_number":"pith:6FFU767A","canonical_record":{"source":{"id":"2407.15236","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-21T17:59:27Z","cross_cats_sorted":[],"title_canon_sha256":"4fce679288ff6e674439a70c221988fa56bc300edefdf021d20c00b16ebc283d","abstract_canon_sha256":"7a394b8812b65a31a1b3b54acf0879196bb61fb8d0377c0948e0af1c1e377086"},"schema_version":"1.0"},"canonical_sha256":"f14b4ffbe0bac660b0c34eab042f6e711705e3f598d02a7cd0a5997fd8f5b97e","source":{"kind":"arxiv","id":"2407.15236","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.15236","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"arxiv_version","alias_value":"2407.15236v1","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15236","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"pith_short_12","alias_value":"6FFU767AXLDG","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"pith_short_16","alias_value":"6FFU767AXLDGBMGD","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"pith_short_8","alias_value":"6FFU767A","created_at":"2026-07-05T08:46:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:6FFU767AXLDGBMGDJ2VQIL3OOE","target":"record","payload":{"canonical_record":{"source":{"id":"2407.15236","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-21T17:59:27Z","cross_cats_sorted":[],"title_canon_sha256":"4fce679288ff6e674439a70c221988fa56bc300edefdf021d20c00b16ebc283d","abstract_canon_sha256":"7a394b8812b65a31a1b3b54acf0879196bb61fb8d0377c0948e0af1c1e377086"},"schema_version":"1.0"},"canonical_sha256":"f14b4ffbe0bac660b0c34eab042f6e711705e3f598d02a7cd0a5997fd8f5b97e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:46:40.554919Z","signature_b64":"Br67S+Sb9h926c/8jzME7GSf4C0s/bBuSohyBGFMiP07iENeWyuhSqpc/ZeKLNxuVZhnvAKAaGNBJA5GMdG6CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f14b4ffbe0bac660b0c34eab042f6e711705e3f598d02a7cd0a5997fd8f5b97e","last_reissued_at":"2026-07-05T08:46:40.554380Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:46:40.554380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.15236","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":"2026-07-05T08:46:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t1QRobHXfqTi9JcwU7AisBcR2ZdxbXOZuWBYpCpPHJCqp0s4/syubHvNBzuv3dt/e4mA+KZ1pp4auoJkpqjnBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T22:44:22.011321Z"},"content_sha256":"1e6ec936d8e8ff0d80f15ee1de3a2af3b7a7ef7df5acb4dd47f5744fd2efad12","schema_version":"1.0","event_id":"sha256:1e6ec936d8e8ff0d80f15ee1de3a2af3b7a7ef7df5acb4dd47f5744fd2efad12"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:6FFU767AXLDGBMGDJ2VQIL3OOE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep State Space Recurrent Neural Networks for Time Series Forecasting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hugo Inzirillo","submitted_at":"2024-07-21T17:59:27Z","abstract_excerpt":"We explore various neural network architectures for modeling the dynamics of the cryptocurrency market. Traditional linear models often fall short in accurately capturing the unique and complex dynamics of this market. In contrast, Deep Neural Networks (DNNs) have demonstrated considerable proficiency in time series forecasting. This papers introduces novel neural network framework that blend the principles of econometric state space models with the dynamic capabilities of Recurrent Neural Networks (RNNs). We propose state space models using Long Short Term Memory (LSTM), Gated Residual Units "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15236","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/2407.15236/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":"2026-07-05T08:46:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S0VDz2/Pe0tmnOLmzJPmgYPkC3EA6RHlIDVuVzgWlBYZuVK4suOoXhwjDFk26jNwOUaC55WOKwcTBhrCX2QMAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T22:44:22.012252Z"},"content_sha256":"819414b70a2eadb9da2f983ccef8deebd207903c50d73f423e1386a714ea82fb","schema_version":"1.0","event_id":"sha256:819414b70a2eadb9da2f983ccef8deebd207903c50d73f423e1386a714ea82fb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6FFU767AXLDGBMGDJ2VQIL3OOE/bundle.json","state_url":"https://pith.science/pith/6FFU767AXLDGBMGDJ2VQIL3OOE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6FFU767AXLDGBMGDJ2VQIL3OOE/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":"2026-08-10T22:44:22Z","links":{"resolver":"https://pith.science/pith/6FFU767AXLDGBMGDJ2VQIL3OOE","bundle":"https://pith.science/pith/6FFU767AXLDGBMGDJ2VQIL3OOE/bundle.json","state":"https://pith.science/pith/6FFU767AXLDGBMGDJ2VQIL3OOE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6FFU767AXLDGBMGDJ2VQIL3OOE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6FFU767AXLDGBMGDJ2VQIL3OOE","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":"7a394b8812b65a31a1b3b54acf0879196bb61fb8d0377c0948e0af1c1e377086","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-21T17:59:27Z","title_canon_sha256":"4fce679288ff6e674439a70c221988fa56bc300edefdf021d20c00b16ebc283d"},"schema_version":"1.0","source":{"id":"2407.15236","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.15236","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"arxiv_version","alias_value":"2407.15236v1","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15236","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"pith_short_12","alias_value":"6FFU767AXLDG","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"pith_short_16","alias_value":"6FFU767AXLDGBMGD","created_at":"2026-07-05T08:46:40Z"},{"alias_kind":"pith_short_8","alias_value":"6FFU767A","created_at":"2026-07-05T08:46:40Z"}],"graph_snapshots":[{"event_id":"sha256:819414b70a2eadb9da2f983ccef8deebd207903c50d73f423e1386a714ea82fb","target":"graph","created_at":"2026-07-05T08:46:40Z","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/2407.15236/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We explore various neural network architectures for modeling the dynamics of the cryptocurrency market. Traditional linear models often fall short in accurately capturing the unique and complex dynamics of this market. In contrast, Deep Neural Networks (DNNs) have demonstrated considerable proficiency in time series forecasting. This papers introduces novel neural network framework that blend the principles of econometric state space models with the dynamic capabilities of Recurrent Neural Networks (RNNs). We propose state space models using Long Short Term Memory (LSTM), Gated Residual Units ","authors_text":"Hugo Inzirillo","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-21T17:59:27Z","title":"Deep State Space Recurrent Neural Networks for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15236","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":"sha256:1e6ec936d8e8ff0d80f15ee1de3a2af3b7a7ef7df5acb4dd47f5744fd2efad12","target":"record","created_at":"2026-07-05T08:46:40Z","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":"7a394b8812b65a31a1b3b54acf0879196bb61fb8d0377c0948e0af1c1e377086","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-21T17:59:27Z","title_canon_sha256":"4fce679288ff6e674439a70c221988fa56bc300edefdf021d20c00b16ebc283d"},"schema_version":"1.0","source":{"id":"2407.15236","kind":"arxiv","version":1}},"canonical_sha256":"f14b4ffbe0bac660b0c34eab042f6e711705e3f598d02a7cd0a5997fd8f5b97e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f14b4ffbe0bac660b0c34eab042f6e711705e3f598d02a7cd0a5997fd8f5b97e","first_computed_at":"2026-07-05T08:46:40.554380Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:46:40.554380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Br67S+Sb9h926c/8jzME7GSf4C0s/bBuSohyBGFMiP07iENeWyuhSqpc/ZeKLNxuVZhnvAKAaGNBJA5GMdG6CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:46:40.554919Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.15236","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1e6ec936d8e8ff0d80f15ee1de3a2af3b7a7ef7df5acb4dd47f5744fd2efad12","sha256:819414b70a2eadb9da2f983ccef8deebd207903c50d73f423e1386a714ea82fb"],"state_sha256":"51f6d757d2bb4dee4c63dd7d8262be6daad9fbce3c3c9f334cda619e90d148fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+8fvxdc/qDxWya1K6Ec23hN6N5Wm7huvgPuysvslQ8su/spyOw/K1ohKNWHmY4F+32UHQV7q93iNoyHh2vmCDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T22:44:22.018553Z","bundle_sha256":"301da68df9a4fcb1770970849c3a2f1637887e38d3f8ec77898de636a1f31921"}}