{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:4PGJ4MA2JLFXNNZ26MNNRZKQJU","short_pith_number":"pith:4PGJ4MA2","schema_version":"1.0","canonical_sha256":"e3cc9e301a4acb76b73af31ad8e5504d3bfa950b73822424c18d6da57905f11d","source":{"kind":"arxiv","id":"1805.03362","version":3},"attestation_state":"computed","paper":{"title":"Attractor Reconstruction by Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"nlin.CD","authors_text":"Brian R. Hunt, Edward Ott, Zhixin Lu","submitted_at":"2018-05-09T03:44:13Z","abstract_excerpt":"A machine-learning approach called \"reservoir computing\" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time series data. We present a theoretical framework that describes conditions under which reservoir computing can create an empirical model capable of skillful short-term forecasts and accurate long-term ergodic behavior. We illustrate this theory through numerical experiments. We also argue that the theory applies to certain other machine learning methods for time series prediction."},"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":"1805.03362","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"nlin.CD","submitted_at":"2018-05-09T03:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"1e527f0ff0327be5c0ede11fad5458d2cc5cbcfcc21622974e9edfc4d7012939","abstract_canon_sha256":"8d5239005e161b70f4f88588910a3d39c9180de5b4f5de28a40260863a32f12f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:24.868306Z","signature_b64":"73bsCjnp1kmEXQ57mDx16abFJ4r3hhi1XwQOj6eX0RQbA4FNmlxSfOX7JFro6bhlPdF3RSz6z6Tx9Bbq85f0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3cc9e301a4acb76b73af31ad8e5504d3bfa950b73822424c18d6da57905f11d","last_reissued_at":"2026-05-18T00:09:24.867845Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:24.867845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Attractor Reconstruction by Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"nlin.CD","authors_text":"Brian R. Hunt, Edward Ott, Zhixin Lu","submitted_at":"2018-05-09T03:44:13Z","abstract_excerpt":"A machine-learning approach called \"reservoir computing\" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time series data. We present a theoretical framework that describes conditions under which reservoir computing can create an empirical model capable of skillful short-term forecasts and accurate long-term ergodic behavior. We illustrate this theory through numerical experiments. We also argue that the theory applies to certain other machine learning methods for time series prediction."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.03362","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1805.03362","created_at":"2026-05-18T00:09:24.867912+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.03362v3","created_at":"2026-05-18T00:09:24.867912+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.03362","created_at":"2026-05-18T00:09:24.867912+00:00"},{"alias_kind":"pith_short_12","alias_value":"4PGJ4MA2JLFX","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_16","alias_value":"4PGJ4MA2JLFXNNZ2","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_8","alias_value":"4PGJ4MA2","created_at":"2026-05-18T12:32:05.422762+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/4PGJ4MA2JLFXNNZ26MNNRZKQJU","json":"https://pith.science/pith/4PGJ4MA2JLFXNNZ26MNNRZKQJU.json","graph_json":"https://pith.science/api/pith-number/4PGJ4MA2JLFXNNZ26MNNRZKQJU/graph.json","events_json":"https://pith.science/api/pith-number/4PGJ4MA2JLFXNNZ26MNNRZKQJU/events.json","paper":"https://pith.science/paper/4PGJ4MA2"},"agent_actions":{"view_html":"https://pith.science/pith/4PGJ4MA2JLFXNNZ26MNNRZKQJU","download_json":"https://pith.science/pith/4PGJ4MA2JLFXNNZ26MNNRZKQJU.json","view_paper":"https://pith.science/paper/4PGJ4MA2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.03362&json=true","fetch_graph":"https://pith.science/api/pith-number/4PGJ4MA2JLFXNNZ26MNNRZKQJU/graph.json","fetch_events":"https://pith.science/api/pith-number/4PGJ4MA2JLFXNNZ26MNNRZKQJU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4PGJ4MA2JLFXNNZ26MNNRZKQJU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4PGJ4MA2JLFXNNZ26MNNRZKQJU/action/storage_attestation","attest_author":"https://pith.science/pith/4PGJ4MA2JLFXNNZ26MNNRZKQJU/action/author_attestation","sign_citation":"https://pith.science/pith/4PGJ4MA2JLFXNNZ26MNNRZKQJU/action/citation_signature","submit_replication":"https://pith.science/pith/4PGJ4MA2JLFXNNZ26MNNRZKQJU/action/replication_record"}},"created_at":"2026-05-18T00:09:24.867912+00:00","updated_at":"2026-05-18T00:09:24.867912+00:00"}