{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LTEW7ZKIETJP37IT66FKFI2YKO","short_pith_number":"pith:LTEW7ZKI","canonical_record":{"source":{"id":"1812.07683","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T22:57:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f1f8e3718a3ed44852d3a41f72ca550dc683d67ca557721c636eadba80cf2924","abstract_canon_sha256":"3b1493448c81c391487767eb068fc4dbab382ecb4d14437bc896906115b88328"},"schema_version":"1.0"},"canonical_sha256":"5cc96fe54824d2fdfd13f78aa2a35853919c566b82175e30ca0a4aef7162ca73","source":{"kind":"arxiv","id":"1812.07683","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07683","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07683v3","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07683","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"pith_short_12","alias_value":"LTEW7ZKIETJP","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"pith_short_16","alias_value":"LTEW7ZKIETJP37IT","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"pith_short_8","alias_value":"LTEW7ZKI","created_at":"2026-07-05T00:08:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LTEW7ZKIETJP37IT66FKFI2YKO","target":"record","payload":{"canonical_record":{"source":{"id":"1812.07683","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T22:57:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f1f8e3718a3ed44852d3a41f72ca550dc683d67ca557721c636eadba80cf2924","abstract_canon_sha256":"3b1493448c81c391487767eb068fc4dbab382ecb4d14437bc896906115b88328"},"schema_version":"1.0"},"canonical_sha256":"5cc96fe54824d2fdfd13f78aa2a35853919c566b82175e30ca0a4aef7162ca73","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:08:25.959677Z","signature_b64":"IBCGJDubgmNaQtmqpn60XYbucFtN34BdUMK2Vdv9mYmTifrJiSQ466ODDPb9w/DA448jfLRRrqSJwVYvY+97Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cc96fe54824d2fdfd13f78aa2a35853919c566b82175e30ca0a4aef7162ca73","last_reissued_at":"2026-07-05T00:08:25.959253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:08:25.959253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.07683","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:08:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ygS5NbvUzwdzZzvACfJjN4W5kFTixK9XM5t/myKqrVuErC+RLesCzr3OgVKgGfIhsUgSWsZz345O1Dz/NaKXDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:14:05.861136Z"},"content_sha256":"55eda5d0d085e2d4a28a6f282a9c2f35c0e74ccbe6b619dc2fd4ea6705cceded","schema_version":"1.0","event_id":"sha256:55eda5d0d085e2d4a28a6f282a9c2f35c0e74ccbe6b619dc2fd4ea6705cceded"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LTEW7ZKIETJP37IT66FKFI2YKO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Anthony S. Maida, Magdy Bayoumi, Nelly Elsayed","submitted_at":"2018-12-18T22:57:46Z","abstract_excerpt":"Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better performance on many time series datasets. The proposed GRU-FCN model outperforms state-of-the-art classification performance in many univariate and multivariate time series datasets. In addition, since the GRU uses a simpler architecture than the LSTM, it has fewer training parame"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07683","kind":"arxiv","version":3},"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/1812.07683/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":"2026-07-05T00:08:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k+ohnCKsShSnwvObTukJUiopxv9FZb8sf+7iR+6lpMG4UnZ/SGGzZjN1IGA32SoI1MmXAz+a8Q1briR6nfDkAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:14:05.861670Z"},"content_sha256":"e192a3743636a6da33eadf405e184188fbaacba8c7e80f985a1cee4da27b0401","schema_version":"1.0","event_id":"sha256:e192a3743636a6da33eadf405e184188fbaacba8c7e80f985a1cee4da27b0401"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LTEW7ZKIETJP37IT66FKFI2YKO/bundle.json","state_url":"https://pith.science/pith/LTEW7ZKIETJP37IT66FKFI2YKO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LTEW7ZKIETJP37IT66FKFI2YKO/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":"2026-08-18T12:14:05Z","links":{"resolver":"https://pith.science/pith/LTEW7ZKIETJP37IT66FKFI2YKO","bundle":"https://pith.science/pith/LTEW7ZKIETJP37IT66FKFI2YKO/bundle.json","state":"https://pith.science/pith/LTEW7ZKIETJP37IT66FKFI2YKO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LTEW7ZKIETJP37IT66FKFI2YKO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LTEW7ZKIETJP37IT66FKFI2YKO","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":"3b1493448c81c391487767eb068fc4dbab382ecb4d14437bc896906115b88328","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T22:57:46Z","title_canon_sha256":"f1f8e3718a3ed44852d3a41f72ca550dc683d67ca557721c636eadba80cf2924"},"schema_version":"1.0","source":{"id":"1812.07683","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07683","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07683v3","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07683","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"pith_short_12","alias_value":"LTEW7ZKIETJP","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"pith_short_16","alias_value":"LTEW7ZKIETJP37IT","created_at":"2026-07-05T00:08:25Z"},{"alias_kind":"pith_short_8","alias_value":"LTEW7ZKI","created_at":"2026-07-05T00:08:25Z"}],"graph_snapshots":[{"event_id":"sha256:e192a3743636a6da33eadf405e184188fbaacba8c7e80f985a1cee4da27b0401","target":"graph","created_at":"2026-07-05T00:08:25Z","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/1812.07683/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better performance on many time series datasets. The proposed GRU-FCN model outperforms state-of-the-art classification performance in many univariate and multivariate time series datasets. In addition, since the GRU uses a simpler architecture than the LSTM, it has fewer training parame","authors_text":"Anthony S. Maida, Magdy Bayoumi, Nelly Elsayed","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T22:57:46Z","title":"Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07683","kind":"arxiv","version":3},"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":"sha256:55eda5d0d085e2d4a28a6f282a9c2f35c0e74ccbe6b619dc2fd4ea6705cceded","target":"record","created_at":"2026-07-05T00:08:25Z","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":"3b1493448c81c391487767eb068fc4dbab382ecb4d14437bc896906115b88328","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-18T22:57:46Z","title_canon_sha256":"f1f8e3718a3ed44852d3a41f72ca550dc683d67ca557721c636eadba80cf2924"},"schema_version":"1.0","source":{"id":"1812.07683","kind":"arxiv","version":3}},"canonical_sha256":"5cc96fe54824d2fdfd13f78aa2a35853919c566b82175e30ca0a4aef7162ca73","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5cc96fe54824d2fdfd13f78aa2a35853919c566b82175e30ca0a4aef7162ca73","first_computed_at":"2026-07-05T00:08:25.959253Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:08:25.959253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IBCGJDubgmNaQtmqpn60XYbucFtN34BdUMK2Vdv9mYmTifrJiSQ466ODDPb9w/DA448jfLRRrqSJwVYvY+97Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:08:25.959677Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.07683","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:55eda5d0d085e2d4a28a6f282a9c2f35c0e74ccbe6b619dc2fd4ea6705cceded","sha256:e192a3743636a6da33eadf405e184188fbaacba8c7e80f985a1cee4da27b0401"],"state_sha256":"399e8158591cf30df132370d77b5c55de9a62eb574dcc2f8f6cd889d2b26ea30"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UA2a0dOcbjsv9eLk3pZtri4bU0O1Iti5gZL1YScl0/ctedTSfyiV6uV/tnNLCAUC2U80OWDwPhV/RZ6JVMC4AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T12:14:05.868666Z","bundle_sha256":"b226dd046a0f13b6f00a5afb69bda75063cddee5bd65d03d710271019a40347c"}}