{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:W4GEIWOJAIZMHI7FB7Q3W6F7VO","short_pith_number":"pith:W4GEIWOJ","canonical_record":{"source":{"id":"1704.02971","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-07T23:50:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8abaf52fa27452ea30611b664bba9b9ed5673de6523b717589132ab0dfbc45c7","abstract_canon_sha256":"94ae0952717e70f86e5373ad87155400ff84c71d6b8b3ee06cfd56ff580a2885"},"schema_version":"1.0"},"canonical_sha256":"b70c4459c90232c3a3e50fe1bb78bfabb227e3d1ae3412356df4608024d1190a","source":{"kind":"arxiv","id":"1704.02971","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.02971","created_at":"2026-05-18T00:38:08Z"},{"alias_kind":"arxiv_version","alias_value":"1704.02971v4","created_at":"2026-05-18T00:38:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02971","created_at":"2026-05-18T00:38:08Z"},{"alias_kind":"pith_short_12","alias_value":"W4GEIWOJAIZM","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"W4GEIWOJAIZMHI7F","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"W4GEIWOJ","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:W4GEIWOJAIZMHI7FB7Q3W6F7VO","target":"record","payload":{"canonical_record":{"source":{"id":"1704.02971","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-07T23:50:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8abaf52fa27452ea30611b664bba9b9ed5673de6523b717589132ab0dfbc45c7","abstract_canon_sha256":"94ae0952717e70f86e5373ad87155400ff84c71d6b8b3ee06cfd56ff580a2885"},"schema_version":"1.0"},"canonical_sha256":"b70c4459c90232c3a3e50fe1bb78bfabb227e3d1ae3412356df4608024d1190a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:08.758033Z","signature_b64":"SNOcCZoDcqRzyG2pZfPqfOfM6U1MGPUqpA5HOTRvPUDLZKZj4NxlqOYhQKMhcxa0MQuUBaPg746/yG2zpCQcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b70c4459c90232c3a3e50fe1bb78bfabb227e3d1ae3412356df4608024d1190a","last_reissued_at":"2026-05-18T00:38:08.757547Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:08.757547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.02971","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tA3HVxO01asxTUsHIus7zqwsjzT7cTXmO3KIakXeN+lzAuLZzUiO+fQ8++RdwAYoIu54e0+QitQ6CPgUc6n1CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T16:42:56.071570Z"},"content_sha256":"7c38439c22c544f6bbede822eb98f70d029907497ebfaa46e8e097e3a8bd7f7c","schema_version":"1.0","event_id":"sha256:7c38439c22c544f6bbede822eb98f70d029907497ebfaa46e8e097e3a8bd7f7c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:W4GEIWOJAIZMHI7FB7Q3W6F7VO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dongjin Song, Garrison Cottrell, Guofei Jiang, Haifeng Chen, Wei Cheng, Yao Qin","submitted_at":"2017-04-07T23:50:09Z","abstract_excerpt":"The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02971","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-18T00:38:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bYSWnavGUiaPbPd0NfxfZJ2W7PsRuzqPq4ZmqdCKwIR+RI8cPOI6YQHAINDtt5kMfjfbMxO6i9Rg817RooljDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T16:42:56.071916Z"},"content_sha256":"64bf5550e82382c9602c8ec6bd180ed98162d48053cea29e0f4f82889aae693a","schema_version":"1.0","event_id":"sha256:64bf5550e82382c9602c8ec6bd180ed98162d48053cea29e0f4f82889aae693a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W4GEIWOJAIZMHI7FB7Q3W6F7VO/bundle.json","state_url":"https://pith.science/pith/W4GEIWOJAIZMHI7FB7Q3W6F7VO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W4GEIWOJAIZMHI7FB7Q3W6F7VO/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":"2026-06-09T16:42:56Z","links":{"resolver":"https://pith.science/pith/W4GEIWOJAIZMHI7FB7Q3W6F7VO","bundle":"https://pith.science/pith/W4GEIWOJAIZMHI7FB7Q3W6F7VO/bundle.json","state":"https://pith.science/pith/W4GEIWOJAIZMHI7FB7Q3W6F7VO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W4GEIWOJAIZMHI7FB7Q3W6F7VO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:W4GEIWOJAIZMHI7FB7Q3W6F7VO","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":"94ae0952717e70f86e5373ad87155400ff84c71d6b8b3ee06cfd56ff580a2885","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-07T23:50:09Z","title_canon_sha256":"8abaf52fa27452ea30611b664bba9b9ed5673de6523b717589132ab0dfbc45c7"},"schema_version":"1.0","source":{"id":"1704.02971","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.02971","created_at":"2026-05-18T00:38:08Z"},{"alias_kind":"arxiv_version","alias_value":"1704.02971v4","created_at":"2026-05-18T00:38:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02971","created_at":"2026-05-18T00:38:08Z"},{"alias_kind":"pith_short_12","alias_value":"W4GEIWOJAIZM","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"W4GEIWOJAIZMHI7F","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"W4GEIWOJ","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:64bf5550e82382c9602c8ec6bd180ed98162d48053cea29e0f4f82889aae693a","target":"graph","created_at":"2026-05-18T00:38:08Z","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"},"paper":{"abstract_excerpt":"The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input ","authors_text":"Dongjin Song, Garrison Cottrell, Guofei Jiang, Haifeng Chen, Wei Cheng, Yao Qin","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-07T23:50:09Z","title":"A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02971","kind":"arxiv","version":4},"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":"sha256:7c38439c22c544f6bbede822eb98f70d029907497ebfaa46e8e097e3a8bd7f7c","target":"record","created_at":"2026-05-18T00:38:08Z","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":"94ae0952717e70f86e5373ad87155400ff84c71d6b8b3ee06cfd56ff580a2885","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-07T23:50:09Z","title_canon_sha256":"8abaf52fa27452ea30611b664bba9b9ed5673de6523b717589132ab0dfbc45c7"},"schema_version":"1.0","source":{"id":"1704.02971","kind":"arxiv","version":4}},"canonical_sha256":"b70c4459c90232c3a3e50fe1bb78bfabb227e3d1ae3412356df4608024d1190a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b70c4459c90232c3a3e50fe1bb78bfabb227e3d1ae3412356df4608024d1190a","first_computed_at":"2026-05-18T00:38:08.757547Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:08.757547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SNOcCZoDcqRzyG2pZfPqfOfM6U1MGPUqpA5HOTRvPUDLZKZj4NxlqOYhQKMhcxa0MQuUBaPg746/yG2zpCQcBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:08.758033Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.02971","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c38439c22c544f6bbede822eb98f70d029907497ebfaa46e8e097e3a8bd7f7c","sha256:64bf5550e82382c9602c8ec6bd180ed98162d48053cea29e0f4f82889aae693a"],"state_sha256":"82c962aff4eab14177672d9dc6d52c5c1c679a94852feeb63cd3a6483a703a18"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/Qhd15WPoNl6IRHRSY6Tzrd7Dgm0m8NQGv31n3KMwSH7mKbW5IcUGnXsOEKNiUO4owAllVac/+U2Tvlau4JdCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T16:42:56.073880Z","bundle_sha256":"fcfcffa72f2e5e004a65ba64c76af49be00938064588d384c1ffb2a542bc872b"}}