{"links":{},"id":75523,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00075523","sets":["934:989:6318:6491"]},"path":["6491"],"owner":"11","recid":"75523","title":["変化点検出を応用した時系列データからの突発現象の前兆検出アルゴリズム"],"pubdate":{"attribute_name":"公開日","attribute_value":"2011-07-20"},"_buckets":{"deposit":"306c5812-499b-4199-a021-f8c6945e77c0"},"_deposit":{"id":"75523","pid":{"type":"depid","value":"75523","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"変化点検出を応用した時系列データからの突発現象の前兆検出アルゴリズム","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"変化点検出を応用した時系列データからの突発現象の前兆検出アルゴリズム"},{"subitem_title":"An Algorithm for Detecting Precursory Events from Time Series Data","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"オリジナル論文","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2011-07-20","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"明治大学先端数理科学インスティチュート"},{"subitem_text_value":"九州大学システム情報科学研究院"},{"subitem_text_value":"明治大学先端数理科学研究科"},{"subitem_text_value":"統計数理研究所"},{"subitem_text_value":"九州大学理学研究院地球惑星科学部門"},{"subitem_text_value":"九州大学宙空環境研究センター"},{"subitem_text_value":"宇宙航空研究開発機構宇宙科学研究所"},{"subitem_text_value":"東北大学惑星プラズマ大気研究センター"},{"subitem_text_value":"九州大学宙空環境研究センター"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Meiji Institute for Advanced Study of Mathematical Sciences","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Information Science and Electrical Engineering, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Advanced Mathematical Sciences, Meiji University","subitem_text_language":"en"},{"subitem_text_value":"The Institute of Statistical Mathematics","subitem_text_language":"en"},{"subitem_text_value":"Earth and Planetary Science, Graduate School of Sciences, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Space Environment Research Center, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency","subitem_text_language":"en"},{"subitem_text_value":"Planetary Plasma and Atmospheric Research Center, Tohoku University","subitem_text_language":"en"},{"subitem_text_value":"Space Environment Research Center, Kyushu 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/75523/files/IPSJ-TOM0403003.pdf"},"date":[{"dateType":"Available","dateValue":"2013-07-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM0403003.pdf","filesize":[{"value":"6.2 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":"b13f7c37-69bf-47f6-90e9-1c4a8c2079c7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2011 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"徳永, 旭将"},{"creatorName":"池田, 大輔"},{"creatorName":"中村, 和幸"},{"creatorName":"樋口, 知之"},{"creatorName":"吉川, 顕正"},{"creatorName":"魚住, 禎司"},{"creatorName":"藤本, 晶子"},{"creatorName":"森岡, 昭"},{"creatorName":"湯元, 清文"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Terumasa, Tokunaga","creatorNameLang":"en"},{"creatorName":"Daisuke, Ikeda","creatorNameLang":"en"},{"creatorName":"Kazuyuki, Nakamura","creatorNameLang":"en"},{"creatorName":"Tomoyuki, Higuchi","creatorNameLang":"en"},{"creatorName":"Akimasa, Yoshikawa","creatorNameLang":"en"},{"creatorName":"Teiji, Uozumi","creatorNameLang":"en"},{"creatorName":"Akiko, Fujimoto","creatorNameLang":"en"},{"creatorName":"Akira, Morioka","creatorNameLang":"en"},{"creatorName":"Kiyohumi, Yumoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"一般に，前兆現象は突発現象にそのものに比べて非常に目立ちにくく，その開始時刻は曖昧である．従来よく用いられてきた変化点検出法を適用した場合，このような微小で緩慢な変化は見逃されやすい．Tokunagaら(2010)では，Ideら(2005)の提案した特異スペクトル分析を応用した変化点検出法（SST）を，多次元データを用いたアルゴリズム（MSST）へと拡張することで，鋭敏に前兆現象の開始時刻を推定できることを示した．MSSTは，緩慢な変化も検出できる鋭敏な手法であるが，実データへの適用では誤検出が問題になる．本稿では，突発現象の大まかな開始時刻をあらかじめ検出し，さらに検出された時刻の前後で前兆現象の開始時刻と終了時刻を個別に探索することで，前兆現象を鋭敏に検出でき，かつMSST単体よりも誤検出を劇的に減少させることができることを示す．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In general, precursory events are observed as minute and less-visible fluctuations preceding an onset of massive fluctuations of extraordinary phenomena. Hence, existing change-point detection methods most likely overlook precursory events. Tokunaga, et al. (2010) extended the method for detecting the change-points, Singular Spectrum Transformation (SST) proposed by Ide, et al. (2005), to the multivariable SST (MSST). Although MSST can detect minute changes, we have to reduce false positives because real world data includes non-stationary trends and measurement noise. In this paper, we propose the algorithm to detect precursory events in an off-line manner. Our algorithm consists of three kinds of change-point detection methods. We show that the number of false positive reduce drastically by combining three different types of change-point detection methods.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"34","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"14","bibliographicIssueDates":{"bibliographicIssueDate":"2011-07-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"4"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-18T23:32:36.369141+00:00","updated":"2025-01-21T21:11:18.843846+00:00"}