{"created":"2023-05-15T11:58:53.623239+00:00","id":5093,"links":{},"metadata":{"_buckets":{"deposit":"8ba56583-9707-4cf9-b2d2-616a90f36000"},"_deposit":{"created_by":3,"id":"5093","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"5093"},"status":"published"},"_oai":{"id":"oai:kyutech.repo.nii.ac.jp:00005093","sets":["8:24"]},"author_link":["203","20518","15851","20519"],"item_21_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2017-07-15","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"349","bibliographicPageStart":"341","bibliographicVolumeNumber":"29","bibliographic_titles":[{"bibliographic_title":"Neural Computing and Applications"}]}]},"item_21_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Recently, we have presented a method of probabilistic prediction of chaotic time series. The method employs learning machines involving strong learners capable of making predictions with desirably long predictable horizons, where, however, usual ensemble mean for making representative prediction is not effective when there are predictions with shorter predictable horizons. Thus, the method selects a representative prediction from the predictions generated by a number of learning machines involving strong learners as follows: first, it obtains plausible predictions holding large similarity of attractors with the training time series and then selects the representative prediction with the largest predictable horizon estimated via LOOCV (leave-one-out cross-validation). The method is also capable of providing average and/or safe estimation of predictable horizon of the representative prediction. We have used CAN2s (competitive associative nets) for learning piecewise linear approximation of nonlinear function as strong learners in our previous study, and this paper employs bagging (bootstrap aggregating) to improve the performance, which enables us to analyze the validity and the effectiveness of the method.","subitem_description_type":"Abstract"}]},"item_21_description_60":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"subitem_description":"Journal Article","subitem_description_type":"Other"}]},"item_21_link_62":{"attribute_name":"研究者情報","attribute_value_mlt":[{"subitem_link_url":"https://hyokadb02.jimu.kyutech.ac.jp/html/12_ja.html"}]},"item_21_publisher_7":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Springer London"}]},"item_21_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1007/s00521-017-3149-7","subitem_relation_type_select":"DOI"}}]},"item_21_rights_13":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"Copyright (c) The Author(s) 2017. This article is an open access publication. Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)"}]},"item_21_select_59":{"attribute_name":"査読の有無","attribute_value_mlt":[{"subitem_select_item":"yes"}]},"item_21_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11006092","subitem_source_identifier_type":"NCID"}]},"item_21_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0941-0643 ","subitem_source_identifier_type":"ISSN"},{"subitem_source_identifier":"1433-3058","subitem_source_identifier_type":"ISSN"}]},"item_21_text_28":{"attribute_name":"論文ID（連携）","attribute_value_mlt":[{"subitem_text_value":"10307743"}]},"item_21_text_36":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Control Engineering, Kyushu Institute of Technology"},{"subitem_text_value":"Department of Control Engineering, Kyushu Institute of Technology"},{"subitem_text_value":"Department of Control Engineering, Kyushu Institute of Technology"},{"subitem_text_value":"Department of Control Engineering, Kyushu Institute of Technology"}]},"item_21_text_63":{"attribute_name":"連携ID","attribute_value_mlt":[{"subitem_text_value":"6274"}]},"item_21_version_type_58":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNameIdentifiers":[],"affiliationNames":[{"affiliationName":""}]}],"creatorNames":[{"creatorName":"Kurogi, Shuichi","creatorNameLang":"en"},{"creatorName":"黒木, 秀一","creatorNameLang":"ja"},{"creatorName":"クロギ, シュウイチ","creatorNameLang":"ja-Kana"}],"familyNames":[{},{},{}],"givenNames":[{},{},{}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"Toidani,  Mitsuki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shigematsu,  Ryosuke"}],"nameIdentifiers":[{}]},{"creatorAffiliations":[{"affiliationNameIdentifiers":[],"affiliationNames":[{"affiliationName":""}]}],"creatorNames":[{"creatorName":"Matsuo, Kazuya","creatorNameLang":"en"},{"creatorName":"松尾, 一矢","creatorNameLang":"ja"},{"creatorName":"マツオ, カズヤ","creatorNameLang":"ja-Kana"}],"familyNames":[{},{},{}],"givenNames":[{},{},{}],"nameIdentifiers":[{},{},{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-08-24"}],"displaytype":"detail","filename":"10.1007_s00521-017-3149-7.pdf","filesize":[{"value":"841.3 kB"}],"format":"application/pdf","licensetype":"license_6","mimetype":"application/pdf","url":{"label":"10.1007_s00521-017-3149-7.pdf","url":"https://kyutech.repo.nii.ac.jp/record/5093/files/10.1007_s00521-017-3149-7.pdf"},"version_id":"d8ac3d40-8448-4b30-80c1-e97e5429e641"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Probabilistic prediction of chaotic time series","subitem_subject_scheme":"Other"},{"subitem_subject":"Long-term　unpredictability","subitem_subject_scheme":"Other"},{"subitem_subject":"Attractors of chaotic time series","subitem_subject_scheme":"Other"},{"subitem_subject":"Leave-one-out cross-validation","subitem_subject_scheme":"Other"},{"subitem_subject":"Estimation of predictable horizon","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon"}]},"item_type_id":"21","owner":"3","path":["24"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-08-24"},"publish_date":"2017-08-24","publish_status":"0","recid":"5093","relation_version_is_last":true,"title":["Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon"],"weko_creator_id":"3","weko_shared_id":3},"updated":"2023-10-25T07:00:54.255510+00:00"}