{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6EB5ZNLTBMRV6K5UR2KRR5DJPE","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":"c7650f4896f1c86f1c62238f7b25349a2c7cf1ad6b146dfbbc9b38607e9bdaec","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T18:24:12Z","title_canon_sha256":"6a348b2c1f834f2c9a326852a222e75de59510b7349be0c1fdbb3cf360caf1b3"},"schema_version":"1.0","source":{"id":"2506.06454","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06454","created_at":"2026-05-26T02:03:51Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06454v2","created_at":"2026-05-26T02:03:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06454","created_at":"2026-05-26T02:03:51Z"},{"alias_kind":"pith_short_12","alias_value":"6EB5ZNLTBMRV","created_at":"2026-05-26T02:03:51Z"},{"alias_kind":"pith_short_16","alias_value":"6EB5ZNLTBMRV6K5U","created_at":"2026-05-26T02:03:51Z"},{"alias_kind":"pith_short_8","alias_value":"6EB5ZNLT","created_at":"2026-05-26T02:03:51Z"}],"graph_snapshots":[{"event_id":"sha256:1b196023e09adf1bfcabb9fef1f7c93bca5be130a9e76a7a9a029be947b0761a","target":"graph","created_at":"2026-05-26T02:03:51Z","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/2506.06454/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future prediction. While deep learning has achieved major success in time series forecasting, many existing approaches do not explicitly model the dynamics. To bridge this gap, we introduce DeepEDM, a framework that integrates nonlinear dynamical systems modeling with deep neural networks. Inspired by empirical dynamic modeling (EDM) and rooted in Takens' theorem, DeepEDM presents a novel deep model that learns a latent space from time-delayed embeddings, and ","authors_text":"Abrar Majeedi, Nada Magdi Elkordi, Satya Sai Srinath Namburi GNVV, Viswanatha Reddy Gajjala, Yin Li","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T18:24:12Z","title":"LETS Forecast: Learning Embedology for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06454","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":"sha256:903f1298d061ee233b70bceeb4e4388d196c9c5d8ddff0d7abac731de907cbc7","target":"record","created_at":"2026-05-26T02:03:51Z","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":"c7650f4896f1c86f1c62238f7b25349a2c7cf1ad6b146dfbbc9b38607e9bdaec","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T18:24:12Z","title_canon_sha256":"6a348b2c1f834f2c9a326852a222e75de59510b7349be0c1fdbb3cf360caf1b3"},"schema_version":"1.0","source":{"id":"2506.06454","kind":"arxiv","version":2}},"canonical_sha256":"f103dcb5730b235f2bb48e9518f469793c1b2165c6f667a8a718e305fa644416","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f103dcb5730b235f2bb48e9518f469793c1b2165c6f667a8a718e305fa644416","first_computed_at":"2026-05-26T02:03:51.559517Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:03:51.559517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cylv7GUsm9m1Raq+4ZV+VkdQbpM8yHigcXCU29lU4YdR4gdow6vPHv1W5sHvh7lLWLQ1xw2AVnvqk8DWYLrZCw==","signature_status":"signed_v1","signed_at":"2026-05-26T02:03:51.560662Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.06454","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:903f1298d061ee233b70bceeb4e4388d196c9c5d8ddff0d7abac731de907cbc7","sha256:1b196023e09adf1bfcabb9fef1f7c93bca5be130a9e76a7a9a029be947b0761a"],"state_sha256":"a4bb05218498e972d3fd068798182ebcc25e233b6d3184c6f3c6b85b23bcd5d6"}