{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218627","sets":["1164:2735:10865:10962"]},"path":["10962"],"owner":"44499","recid":"218627","title":["代謝ネットワーク解析に向けた予測誤差の状態空間を用いる時系列因果推論法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"924fdd2f-f4c7-41d6-b2be-0782a4616a83"},"_deposit":{"id":"218627","pid":{"type":"depid","value":"218627","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"代謝ネットワーク解析に向けた予測誤差の状態空間を用いる時系列因果推論法の提案","author_link":["569020","569019"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"代謝ネットワーク解析に向けた予測誤差の状態空間を用いる時系列因果推論法の提案"}]},"item_type_id":"4","publish_date":"2022-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"立命館大・情報理工"},{"subitem_text_value":"立命館大・情報理工"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"Ritsumeikan University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/218627/files/IPSJ-MPS22138057.pdf","label":"IPSJ-MPS22138057.pdf"},"date":[{"dateType":"Available","dateValue":"2024-06-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS22138057.pdf","filesize":[{"value":"1.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c8f92ad9-5427-4623-b90d-30b815b671dd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大山, 鷹志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"遠里, 由佳子"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究は，生体内の代謝物などを計測して得られる非線形な時系列から，制御因子を推定することを目指し，新しい因果推論法の提案を目的とする．非線形でノイズが強い 2 つの時系列に対する因果推論の従来法に Non-Parametric Multiplicative Regression Granger 因果性テスト（NPMR）がある．NPMR は各時系列を埋め込んで作成した状態空間間で予測時系列を作成する．そこで，NPMR の状態空間で自己回帰により求めた予測時系列と元時系列との誤差から，さらに状態空間を作成し推論を行う手法を提案した．因果関係の強弱を調整できる結合ロジスティックマップで生成した短時系列（N=25）に対し，提案手法の推論精度が 71.0% となり NPMR を上回ることを確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"57","bibliographicVolumeNumber":"2022-MPS-138"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:18:58.940548+00:00","updated":"2025-01-19T15:06:11.441297+00:00","id":218627}