{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:SEH27OLIIQN6GVWNN6DYDP6GLT","short_pith_number":"pith:SEH27OLI","canonical_record":{"source":{"id":"2202.03944","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-08T15:51:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d799f75007d75264f08169eb8b67a6e4eda5b3df11269024d9e42fe9bcdef10d","abstract_canon_sha256":"ca5ba7eeebd81f22d77da9585699a89c9b68c694884d41f64c0b5ae14f7f9e70"},"schema_version":"1.0"},"canonical_sha256":"910fafb968441be356cd6f8781bfc65cf8d35e31423823e232e6a6401d35dd3e","source":{"kind":"arxiv","id":"2202.03944","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.03944","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"arxiv_version","alias_value":"2202.03944v2","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.03944","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"pith_short_12","alias_value":"SEH27OLIIQN6","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"pith_short_16","alias_value":"SEH27OLIIQN6GVWN","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"pith_short_8","alias_value":"SEH27OLI","created_at":"2026-07-05T03:58:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:SEH27OLIIQN6GVWNN6DYDP6GLT","target":"record","payload":{"canonical_record":{"source":{"id":"2202.03944","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-08T15:51:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d799f75007d75264f08169eb8b67a6e4eda5b3df11269024d9e42fe9bcdef10d","abstract_canon_sha256":"ca5ba7eeebd81f22d77da9585699a89c9b68c694884d41f64c0b5ae14f7f9e70"},"schema_version":"1.0"},"canonical_sha256":"910fafb968441be356cd6f8781bfc65cf8d35e31423823e232e6a6401d35dd3e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:58:20.827215Z","signature_b64":"QEXcM31TA9mPihBsQwCXt4fx6CONSuav5nY6isuY4qSoF6DRyTWCinOwgk62uCmaWCPeGPyh4kFdejP6bxnIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"910fafb968441be356cd6f8781bfc65cf8d35e31423823e232e6a6401d35dd3e","last_reissued_at":"2026-07-05T03:58:20.826846Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:58:20.826846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.03944","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:58:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nmPnuultj6lOcAnSFH5SDGLkDoxwcXOdSLlD8gDy5svESMLgwKvaKRdHcVOSMW9uFKEtt6WxBU/sdtff+5BDBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T23:27:13.761424Z"},"content_sha256":"4beec2880caf2c8d4d0c40300dafeaa4495d37ee8b0e3b1829e0fa21aa074472","schema_version":"1.0","event_id":"sha256:4beec2880caf2c8d4d0c40300dafeaa4495d37ee8b0e3b1829e0fa21aa074472"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:SEH27OLIIQN6GVWNN6DYDP6GLT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Detecting Anomalies within Time Series using Local Neural Transformations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chen Qiu, Decky Aspandi Latif, Maja Rudolph, Marius Kloft, Steffen Staab, Stephan Mandt, Tim Schneider","submitted_at":"2022-02-08T15:51:31Z","abstract_excerpt":"We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology. The method is based on self-supervised deep learning that has played a key role in facilitating deep anomaly detection on images, where powerful image transformations are available. However, such transformations are widely unavailable for time series. Addressing this, we develop Local Neural Transformations(LNT), a method learning local transformations of time series from data. The method p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.03944","kind":"arxiv","version":2},"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/2202.03944/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":"2026-07-05T03:58:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QtRbXkKFtCT0fyrMGnQfgDRe9IdEh6VeJugSfxSwgSqKH5lROPlUDa0sRArClZXNwkGFhIpAUq0r7giiqDPWDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T23:27:13.761795Z"},"content_sha256":"311bb5899494a9aea32e5150cdcb4f99fc7cba95c0a14d0cb20b60890bb12f51","schema_version":"1.0","event_id":"sha256:311bb5899494a9aea32e5150cdcb4f99fc7cba95c0a14d0cb20b60890bb12f51"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SEH27OLIIQN6GVWNN6DYDP6GLT/bundle.json","state_url":"https://pith.science/pith/SEH27OLIIQN6GVWNN6DYDP6GLT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SEH27OLIIQN6GVWNN6DYDP6GLT/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":"2026-07-16T23:27:13Z","links":{"resolver":"https://pith.science/pith/SEH27OLIIQN6GVWNN6DYDP6GLT","bundle":"https://pith.science/pith/SEH27OLIIQN6GVWNN6DYDP6GLT/bundle.json","state":"https://pith.science/pith/SEH27OLIIQN6GVWNN6DYDP6GLT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SEH27OLIIQN6GVWNN6DYDP6GLT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:SEH27OLIIQN6GVWNN6DYDP6GLT","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":"ca5ba7eeebd81f22d77da9585699a89c9b68c694884d41f64c0b5ae14f7f9e70","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-08T15:51:31Z","title_canon_sha256":"d799f75007d75264f08169eb8b67a6e4eda5b3df11269024d9e42fe9bcdef10d"},"schema_version":"1.0","source":{"id":"2202.03944","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.03944","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"arxiv_version","alias_value":"2202.03944v2","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.03944","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"pith_short_12","alias_value":"SEH27OLIIQN6","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"pith_short_16","alias_value":"SEH27OLIIQN6GVWN","created_at":"2026-07-05T03:58:20Z"},{"alias_kind":"pith_short_8","alias_value":"SEH27OLI","created_at":"2026-07-05T03:58:20Z"}],"graph_snapshots":[{"event_id":"sha256:311bb5899494a9aea32e5150cdcb4f99fc7cba95c0a14d0cb20b60890bb12f51","target":"graph","created_at":"2026-07-05T03:58:20Z","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/2202.03944/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology. The method is based on self-supervised deep learning that has played a key role in facilitating deep anomaly detection on images, where powerful image transformations are available. However, such transformations are widely unavailable for time series. Addressing this, we develop Local Neural Transformations(LNT), a method learning local transformations of time series from data. The method p","authors_text":"Chen Qiu, Decky Aspandi Latif, Maja Rudolph, Marius Kloft, Steffen Staab, Stephan Mandt, Tim Schneider","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-08T15:51:31Z","title":"Detecting Anomalies within Time Series using Local Neural Transformations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.03944","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":"sha256:4beec2880caf2c8d4d0c40300dafeaa4495d37ee8b0e3b1829e0fa21aa074472","target":"record","created_at":"2026-07-05T03:58:20Z","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":"ca5ba7eeebd81f22d77da9585699a89c9b68c694884d41f64c0b5ae14f7f9e70","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-08T15:51:31Z","title_canon_sha256":"d799f75007d75264f08169eb8b67a6e4eda5b3df11269024d9e42fe9bcdef10d"},"schema_version":"1.0","source":{"id":"2202.03944","kind":"arxiv","version":2}},"canonical_sha256":"910fafb968441be356cd6f8781bfc65cf8d35e31423823e232e6a6401d35dd3e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"910fafb968441be356cd6f8781bfc65cf8d35e31423823e232e6a6401d35dd3e","first_computed_at":"2026-07-05T03:58:20.826846Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:58:20.826846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QEXcM31TA9mPihBsQwCXt4fx6CONSuav5nY6isuY4qSoF6DRyTWCinOwgk62uCmaWCPeGPyh4kFdejP6bxnIDA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:58:20.827215Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.03944","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4beec2880caf2c8d4d0c40300dafeaa4495d37ee8b0e3b1829e0fa21aa074472","sha256:311bb5899494a9aea32e5150cdcb4f99fc7cba95c0a14d0cb20b60890bb12f51"],"state_sha256":"6e31c5a54724887b8b723cca475ac73f7d9d64b265ff475f2856ab871739745a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JtlCywso40G8qZWZVXkzZ6wUdQt+J0wrpDyEuJryUpaQZQwA0NjM3dqGX8p+o08Ti8UW//VciAfMunxaAuP+Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-16T23:27:13.763986Z","bundle_sha256":"9b708d9957072c2f566034c8e23a508f3ac5b48d6c3d569bf9c07c3ca568a03c"}}