{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:MDDWPBNS24PWAHRO5D3XGWQPMJ","short_pith_number":"pith:MDDWPBNS","schema_version":"1.0","canonical_sha256":"60c76785b2d71f601e2ee8f7735a0f6262cf2e5e25c15a158c35c0f442bc8bed","source":{"kind":"arxiv","id":"1409.5974","version":1},"attestation_state":"computed","paper":{"title":"A high dimensional delay selection for the reconstruction of proper Phase Space with Cross auto-correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"nlin.CD","authors_text":"D. K. Bhattacharya, Sanjay Kumar Palit, Sayan Mukherjee","submitted_at":"2014-09-21T11:43:20Z","abstract_excerpt":"For the purpose of phase space reconstruction from nonlinear time series, delay selection is one of the most vital criteria. This is normally done by using a general measure viz., mutual information (MI). However, in that case, the delay selection is limited to the estimation of a single delay using MI between two variables only. The corresponding reconstructed phase space is also not satisfactory. To overcome the situation, a high-dimensional estimator of the MI is used; it selects more than one delay between more than two variables. The quality of the reconstructed phase space is tested by s"},"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":"1409.5974","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"nlin.CD","submitted_at":"2014-09-21T11:43:20Z","cross_cats_sorted":[],"title_canon_sha256":"779ad842efdd6679d351239b4541b224eceb33ad13792bfb4486c9ed1974aadb","abstract_canon_sha256":"5fe5b8699228df14fa3a423390c864bb2d9838b4c0713d27170c8401d402ad01"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:42:14.024102Z","signature_b64":"bOWd6ufMGYVprzN5rtzXBeF/dL8r6X55XzlAnvAkHtyozRmN3FxKidlB9lHfFqyq3PjZKhgAdXh+gCHtqdR7Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60c76785b2d71f601e2ee8f7735a0f6262cf2e5e25c15a158c35c0f442bc8bed","last_reissued_at":"2026-05-18T02:42:14.023739Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:42:14.023739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A high dimensional delay selection for the reconstruction of proper Phase Space with Cross auto-correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"nlin.CD","authors_text":"D. K. Bhattacharya, Sanjay Kumar Palit, Sayan Mukherjee","submitted_at":"2014-09-21T11:43:20Z","abstract_excerpt":"For the purpose of phase space reconstruction from nonlinear time series, delay selection is one of the most vital criteria. This is normally done by using a general measure viz., mutual information (MI). However, in that case, the delay selection is limited to the estimation of a single delay using MI between two variables only. The corresponding reconstructed phase space is also not satisfactory. To overcome the situation, a high-dimensional estimator of the MI is used; it selects more than one delay between more than two variables. The quality of the reconstructed phase space is tested by s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.5974","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":""},"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":"1409.5974","created_at":"2026-05-18T02:42:14.023795+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.5974v1","created_at":"2026-05-18T02:42:14.023795+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.5974","created_at":"2026-05-18T02:42:14.023795+00:00"},{"alias_kind":"pith_short_12","alias_value":"MDDWPBNS24PW","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_16","alias_value":"MDDWPBNS24PWAHRO","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_8","alias_value":"MDDWPBNS","created_at":"2026-05-18T12:28:38.356838+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/MDDWPBNS24PWAHRO5D3XGWQPMJ","json":"https://pith.science/pith/MDDWPBNS24PWAHRO5D3XGWQPMJ.json","graph_json":"https://pith.science/api/pith-number/MDDWPBNS24PWAHRO5D3XGWQPMJ/graph.json","events_json":"https://pith.science/api/pith-number/MDDWPBNS24PWAHRO5D3XGWQPMJ/events.json","paper":"https://pith.science/paper/MDDWPBNS"},"agent_actions":{"view_html":"https://pith.science/pith/MDDWPBNS24PWAHRO5D3XGWQPMJ","download_json":"https://pith.science/pith/MDDWPBNS24PWAHRO5D3XGWQPMJ.json","view_paper":"https://pith.science/paper/MDDWPBNS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.5974&json=true","fetch_graph":"https://pith.science/api/pith-number/MDDWPBNS24PWAHRO5D3XGWQPMJ/graph.json","fetch_events":"https://pith.science/api/pith-number/MDDWPBNS24PWAHRO5D3XGWQPMJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MDDWPBNS24PWAHRO5D3XGWQPMJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MDDWPBNS24PWAHRO5D3XGWQPMJ/action/storage_attestation","attest_author":"https://pith.science/pith/MDDWPBNS24PWAHRO5D3XGWQPMJ/action/author_attestation","sign_citation":"https://pith.science/pith/MDDWPBNS24PWAHRO5D3XGWQPMJ/action/citation_signature","submit_replication":"https://pith.science/pith/MDDWPBNS24PWAHRO5D3XGWQPMJ/action/replication_record"}},"created_at":"2026-05-18T02:42:14.023795+00:00","updated_at":"2026-05-18T02:42:14.023795+00:00"}