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Abarbanel, Analysis of Observed Chaotic Data, Springer, 1996","work_id":"2e6480de-ea4a-4bb0-9da9-cf9ab183f539","year":1996}],"snapshot_sha256":"60f9f28fc4ede0538401492f9bae42186bf81651b70f41b6247363ceb1a789cb"},"source":{"id":"2605.17282","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T23:10:49.359078Z","id":"14a27f32-e14d-442b-96d3-6ca4fd9544cb","model_set":{"reader":"grok-4.3"},"one_line_summary":"FEG-Pro estimates finite-horizon forecast-error growth slopes from scalar time series via kNN multi-horizon forecasting as proxies for largest Lyapunov exponents, while extracting additional profile descriptors.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A forecast-error growth slope from scalar time series estimates the largest Lyapunov exponent when the growth curve shows a quasi-linear regime.","strongest_claim":"When the error-growth curve supports a quasi-linear regime, the finite-horizon forecast-error growth slope lambda_FEG can be compared with reference largest Lyapunov exponents as an estimate of the dominant instability rate.","weakest_assumption":"The forecasting procedure (autocorrelation-guided sparse histories plus distance-weighted k-nearest-neighbor multi-horizon prediction) accurately captures the local expansion rates of the underlying dynamics so that the resulting error-growth slope approximates the largest Lyapunov exponent in quasi-linear regimes."}},"verdict_id":"14a27f32-e14d-442b-96d3-6ca4fd9544cb"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fbdd6a20bd34945bd72f7f23638ba07f4364bcdd1727471e6d8f01b0ee043696","target":"record","created_at":"2026-05-20T00:03:49Z","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":"9e295aa1651e9d6ed504b9fe7446e0687312cc478942f788850bdd529384bd93","cross_cats_sorted":["cs.LG","math.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"nlin.CD","submitted_at":"2026-05-17T06:38:37Z","title_canon_sha256":"568e31487a2bf5b4b4894bb6cbc122b8aac1d4c57ad6da60c4414a2f4d738135"},"schema_version":"1.0","source":{"id":"2605.17282","kind":"arxiv","version":1}},"canonical_sha256":"89e67d02e67c4f5ff30f104f9d6c7a2245bfd88b4e6b329aa3be0e361f1f9998","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89e67d02e67c4f5ff30f104f9d6c7a2245bfd88b4e6b329aa3be0e361f1f9998","first_computed_at":"2026-05-20T00:03:49.641269Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:49.641269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lKCZEleKUSiiMzG7B+Py1ylCQUhF1PIG5jE+N75g44BrmfCP4/1wLphbQ1uqJP9NB9A5d0aKFdwhDcaJNQcuAQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:49.642151Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17282","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbdd6a20bd34945bd72f7f23638ba07f4364bcdd1727471e6d8f01b0ee043696","sha256:177b8abca59d2f9e54ab5810f39c43743b81f24c519123856c3b2781ff3ba31c"],"state_sha256":"33e1ba37f23df7537181078c556438d92d94a3839c4d12fc78f195ae62cffcee"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LhgcsSF9yhCUs4sS5xbM6LxOMkvKulTqPYFqCek/XobGC0/U6coTdR7v8822uNtVMaWVOk1Ked9Ke7QBwXESDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T13:52:50.123602Z","bundle_sha256":"0b7f47a322bc631d5bd1180e71e172dc21c489b662857af93b08a5620bd46ee3"}}