{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VIEPRV2NJKG6ZJHRVLXNTGU3FI","short_pith_number":"pith:VIEPRV2N","schema_version":"1.0","canonical_sha256":"aa08f8d74d4a8deca4f1aaeed99a9b2a3699e30db3829eef5ee971ab6e9c8b09","source":{"kind":"arxiv","id":"2605.19311","version":1},"attestation_state":"computed","paper":{"title":"An Objective Performance Evaluation of the LSTM Networks in Time Series Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Balakumar Balasingam, Sooraj Sunil","submitted_at":"2026-05-19T03:47:01Z","abstract_excerpt":"The rapid adoption of deep learning has increasingly led to data-driven models replacing classical model-based algorithms, even in domains governed by well-understood physical laws. While data-driven models, such as long short-term memory (LSTM) networks, have become a popular choice for time-series analysis, their performance relative to model-based approaches in structured environments is rarely evaluated objectively. This paper presents a performance evaluation framework comparing an LSTM classifier against a model-based expectation maximization (EM) classifier for binary time-series classi"},"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":"2605.19311","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-19T03:47:01Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"ce2343523e5f2816159036d84d9d90565811b0c9f98160649eff5122788eed23","abstract_canon_sha256":"28846527819ad7bcc91c0a9fe929f5ce839baac924586fff0924581891e9aa65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:05:38.738529Z","signature_b64":"uDbyPudWt+0moaXzirEfWR5bM8egrSUKnyZ/FCWEB600CFCuuepxZOokTMVPQYnxFALkrG5vKp6IgRAaA7q1BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa08f8d74d4a8deca4f1aaeed99a9b2a3699e30db3829eef5ee971ab6e9c8b09","last_reissued_at":"2026-05-20T01:05:38.737952Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:05:38.737952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Objective Performance Evaluation of the LSTM Networks in Time Series Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Balakumar Balasingam, Sooraj Sunil","submitted_at":"2026-05-19T03:47:01Z","abstract_excerpt":"The rapid adoption of deep learning has increasingly led to data-driven models replacing classical model-based algorithms, even in domains governed by well-understood physical laws. While data-driven models, such as long short-term memory (LSTM) networks, have become a popular choice for time-series analysis, their performance relative to model-based approaches in structured environments is rarely evaluated objectively. This paper presents a performance evaluation framework comparing an LSTM classifier against a model-based expectation maximization (EM) classifier for binary time-series classi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19311","kind":"arxiv","version":1},"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/2605.19311/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":"2605.19311","created_at":"2026-05-20T01:05:38.738035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.19311v1","created_at":"2026-05-20T01:05:38.738035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19311","created_at":"2026-05-20T01:05:38.738035+00:00"},{"alias_kind":"pith_short_12","alias_value":"VIEPRV2NJKG6","created_at":"2026-05-20T01:05:38.738035+00:00"},{"alias_kind":"pith_short_16","alias_value":"VIEPRV2NJKG6ZJHR","created_at":"2026-05-20T01:05:38.738035+00:00"},{"alias_kind":"pith_short_8","alias_value":"VIEPRV2N","created_at":"2026-05-20T01:05:38.738035+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/VIEPRV2NJKG6ZJHRVLXNTGU3FI","json":"https://pith.science/pith/VIEPRV2NJKG6ZJHRVLXNTGU3FI.json","graph_json":"https://pith.science/api/pith-number/VIEPRV2NJKG6ZJHRVLXNTGU3FI/graph.json","events_json":"https://pith.science/api/pith-number/VIEPRV2NJKG6ZJHRVLXNTGU3FI/events.json","paper":"https://pith.science/paper/VIEPRV2N"},"agent_actions":{"view_html":"https://pith.science/pith/VIEPRV2NJKG6ZJHRVLXNTGU3FI","download_json":"https://pith.science/pith/VIEPRV2NJKG6ZJHRVLXNTGU3FI.json","view_paper":"https://pith.science/paper/VIEPRV2N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.19311&json=true","fetch_graph":"https://pith.science/api/pith-number/VIEPRV2NJKG6ZJHRVLXNTGU3FI/graph.json","fetch_events":"https://pith.science/api/pith-number/VIEPRV2NJKG6ZJHRVLXNTGU3FI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VIEPRV2NJKG6ZJHRVLXNTGU3FI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VIEPRV2NJKG6ZJHRVLXNTGU3FI/action/storage_attestation","attest_author":"https://pith.science/pith/VIEPRV2NJKG6ZJHRVLXNTGU3FI/action/author_attestation","sign_citation":"https://pith.science/pith/VIEPRV2NJKG6ZJHRVLXNTGU3FI/action/citation_signature","submit_replication":"https://pith.science/pith/VIEPRV2NJKG6ZJHRVLXNTGU3FI/action/replication_record"}},"created_at":"2026-05-20T01:05:38.738035+00:00","updated_at":"2026-05-20T01:05:38.738035+00:00"}