{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FIXTSGBXHKBMM7YNF2DXXWICBS","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":"36cafa325d02fcfc350c1ef8fdc9b9d5400f5df2d5782c584df5712c6308af29","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-15T17:19:44Z","title_canon_sha256":"d61cb11608cadf9c92b8814abaff3c28508305b6cf476af1c3eda3c782d04773"},"schema_version":"1.0","source":{"id":"2202.07586","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.07586","created_at":"2026-07-05T04:00:04Z"},{"alias_kind":"arxiv_version","alias_value":"2202.07586v2","created_at":"2026-07-05T04:00:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.07586","created_at":"2026-07-05T04:00:04Z"},{"alias_kind":"pith_short_12","alias_value":"FIXTSGBXHKBM","created_at":"2026-07-05T04:00:04Z"},{"alias_kind":"pith_short_16","alias_value":"FIXTSGBXHKBMM7YN","created_at":"2026-07-05T04:00:04Z"},{"alias_kind":"pith_short_8","alias_value":"FIXTSGBX","created_at":"2026-07-05T04:00:04Z"}],"graph_snapshots":[{"event_id":"sha256:9638ec02a82fe609fe92c5f7182bb1e9e3ece28f10b6d4051768f785b997b730","target":"graph","created_at":"2026-07-05T04:00:04Z","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/2202.07586/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets. Among reconstruction-based models, most previous work has focused on Variational Autoencoders and Generative Adversarial Networks. This work presents DGHL, a new family of generative models for time series anomaly detection, trained by maximizing the observed likelihood by posterior sampling and alternating back-propagation. A top-down Convolution Network maps a novel hierarchical latent space to time series windows","authors_text":"Cristian Challu, Laurent Callot, Peihong Jiang, Ying Nian Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-15T17:19:44Z","title":"Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.07586","kind":"arxiv","version":2},"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:c36d44c8ee81264aa0eb8f7ad0fc2671d995a91fd0030f3f9a5dec78e9ee5347","target":"record","created_at":"2026-07-05T04:00:04Z","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":"36cafa325d02fcfc350c1ef8fdc9b9d5400f5df2d5782c584df5712c6308af29","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-15T17:19:44Z","title_canon_sha256":"d61cb11608cadf9c92b8814abaff3c28508305b6cf476af1c3eda3c782d04773"},"schema_version":"1.0","source":{"id":"2202.07586","kind":"arxiv","version":2}},"canonical_sha256":"2a2f3918373a82c67f0d2e877bd9020c85f4985828528b5211c38df3ab6b3097","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2a2f3918373a82c67f0d2e877bd9020c85f4985828528b5211c38df3ab6b3097","first_computed_at":"2026-07-05T04:00:04.767216Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:00:04.767216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GmKJ7Y5SYo6CvLGAPgyb2kdEJpGckGm65cFfVbDezF9na+ckobar0LMvuYx4ikZslZq57jOs7+5C40TQ7L2NDg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:00:04.767619Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.07586","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c36d44c8ee81264aa0eb8f7ad0fc2671d995a91fd0030f3f9a5dec78e9ee5347","sha256:9638ec02a82fe609fe92c5f7182bb1e9e3ece28f10b6d4051768f785b997b730"],"state_sha256":"05d3ba47834a2de03407ed2b6b7d1ca9d8114bf6952d40b27f4db6225e1f3b24"}