{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:Y76MSZW35GU7MBYQVE66WFXNC7","short_pith_number":"pith:Y76MSZW3","schema_version":"1.0","canonical_sha256":"c7fcc966dbe9a9f60710a93deb16ed17f88710d88b333d0a2dde65f66f5abbc1","source":{"kind":"arxiv","id":"2308.06663","version":2},"attestation_state":"computed","paper":{"title":"ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Md Abul Bashar, Richi Nayak","submitted_at":"2023-08-13T02:17:19Z","abstract_excerpt":"Anomaly detection in time series data, to identify points that deviate from normal behaviour, is a common problem in various domains such as manufacturing, medical imaging, and cybersecurity. Recently, Generative Adversarial Networks (GANs) are shown to be effective in detecting anomalies in time series data. The neural network architecture of GANs (i.e. Generator and Discriminator) can significantly improve anomaly detection accuracy. In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both u"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2308.06663","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-13T02:17:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bdf2f4626074f7b88a69440e2832581632a82d9ad7aac59de4b5a91d08ff17d2","abstract_canon_sha256":"13c9981a7c3d3ad4cdcb9c9e5f2065e02621141e71c4fe38439b1f8c4ea976b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:54.859889Z","signature_b64":"RZ06b2PHQHMMauYN+Vs0eN9xWtRz1G/IM0R+C5WlaVEDkGbeXI6qmUJUmwD4ps11zuFJJ+EFhMWq3Ir2RlogDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7fcc966dbe9a9f60710a93deb16ed17f88710d88b333d0a2dde65f66f5abbc1","last_reissued_at":"2026-07-05T11:08:54.859329Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:54.859329Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Md Abul Bashar, Richi Nayak","submitted_at":"2023-08-13T02:17:19Z","abstract_excerpt":"Anomaly detection in time series data, to identify points that deviate from normal behaviour, is a common problem in various domains such as manufacturing, medical imaging, and cybersecurity. Recently, Generative Adversarial Networks (GANs) are shown to be effective in detecting anomalies in time series data. The neural network architecture of GANs (i.e. Generator and Discriminator) can significantly improve anomaly detection accuracy. In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.06663","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/2308.06663/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2308.06663","created_at":"2026-07-05T11:08:54.859384+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.06663v2","created_at":"2026-07-05T11:08:54.859384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.06663","created_at":"2026-07-05T11:08:54.859384+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y76MSZW35GU7","created_at":"2026-07-05T11:08:54.859384+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y76MSZW35GU7MBYQ","created_at":"2026-07-05T11:08:54.859384+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y76MSZW3","created_at":"2026-07-05T11:08:54.859384+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7","json":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7.json","graph_json":"https://pith.science/api/pith-number/Y76MSZW35GU7MBYQVE66WFXNC7/graph.json","events_json":"https://pith.science/api/pith-number/Y76MSZW35GU7MBYQVE66WFXNC7/events.json","paper":"https://pith.science/paper/Y76MSZW3"},"agent_actions":{"view_html":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7","download_json":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7.json","view_paper":"https://pith.science/paper/Y76MSZW3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.06663&json=true","fetch_graph":"https://pith.science/api/pith-number/Y76MSZW35GU7MBYQVE66WFXNC7/graph.json","fetch_events":"https://pith.science/api/pith-number/Y76MSZW35GU7MBYQVE66WFXNC7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7/action/storage_attestation","attest_author":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7/action/author_attestation","sign_citation":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7/action/citation_signature","submit_replication":"https://pith.science/pith/Y76MSZW35GU7MBYQVE66WFXNC7/action/replication_record"}},"created_at":"2026-07-05T11:08:54.859384+00:00","updated_at":"2026-07-05T11:08:54.859384+00:00"}