{"created":"2023-05-15T14:24:19.078768+00:00","id":5544,"links":{},"metadata":{"_buckets":{"deposit":"cc592cfa-4b83-41fe-aa44-7348dc224e0d"},"_deposit":{"created_by":15,"id":"5544","owners":[15],"pid":{"revision_id":0,"type":"depid","value":"5544"},"status":"published"},"_oai":{"id":"oai:glim-re.repo.nii.ac.jp:00005544","sets":["1253:1361:16:1443"]},"author_link":["34015","48598","48599"],"item_10002_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-07","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicPageEnd":"140","bibliographicPageStart":"127","bibliographicVolumeNumber":"59","bibliographic_titles":[{"bibliographic_title":"學習院大學經濟論集","bibliographic_titleLang":"ja"},{"bibliographic_title":"The journal of Faculty of Economics, Gakushuin University","bibliographic_titleLang":"en"}]}]},"item_10002_description_19":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_10002_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"In the paper, we propose an amplitude-based time series data clustering method. When we analyze the trend index movement in economy, shape-based clustering does not work well because the standardization/z-normalization is required in advance on the input data and the standardization removes the amplitude/variance information from the original data. Then, the flat fluctuation may often become a large-variance fluctuation by the standardization, which is a problem. To solve the problem, we proposed a method by Amplitude-based time series data clustering method which uses Euclidean distance of Euclidean distances as the distance measurement. In the paper, we investigate the performance of the method, using the real stock prices data. The data are the indexed growth rate patterns of stock prices. Our proposed method could divide the companies’ stocks as we humans did, and the result met our requirements. The proposed amplitude-based time series data clustering method is helpful in economic indexed growth data clustering.","subitem_description_type":"Abstract"}]},"item_10002_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"学習院大学経済学会","subitem_publisher_language":"ja"}]},"item_10002_source_id_11":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00038827","subitem_source_identifier_type":"NCID"}]},"item_10002_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"00163953","subitem_source_identifier_type":"PISSN"}]},"item_10002_version_type_20":{"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":[{"creatorNames":[{"creatorName":"白田, 由香利","creatorNameLang":"ja"},{"creatorName":"シロタ, ユカリ","creatorNameLang":"ja-Kana"},{"creatorName":"Shirota, Yukari","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"34015","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"1000030337901","nameIdentifierScheme":"CiNii ID","nameIdentifierURI":"http://ci.nii.ac.jp/nrid/1000030337901"},{"nameIdentifier":"30337901","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://kaken.nii.ac.jp/ja/search/?qm=30337901"},{"nameIdentifier":"DA04685035","nameIdentifierScheme":"AID","nameIdentifierURI":"https://ci.nii.ac.jp/author/DA04685035"}]},{"creatorNames":[{"creatorName":"Basabi, Chakraborty","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"48598","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Basabi, Chakraborty","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"48599","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2023-01-17"}],"displaytype":"detail","filename":"keizai_59_2_127_140.pdf","filesize":[{"value":"3.3 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"keizai_59_2_127_140.pdf","objectType":"fulltext","url":"https://glim-re.repo.nii.ac.jp/record/5544/files/keizai_59_2_127_140.pdf"},"version_id":"f34d0dfb-e7bf-495a-84d7-d6d880b836fd"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"amplitude-based time series data clustering","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Euclidean distance","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"k-Shape","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"DTW","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"hierarchical clustering","subitem_subject_language":"en","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":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Amplitude-Based Time Series Data Clustering Method","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Amplitude-Based Time Series Data Clustering Method","subitem_title_language":"en"}]},"item_type_id":"10002","owner":"15","path":["1443"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-01-17"},"publish_date":"2023-01-17","publish_status":"0","recid":"5544","relation_version_is_last":true,"title":["Amplitude-Based Time Series Data Clustering Method"],"weko_creator_id":"15","weko_shared_id":-1},"updated":"2023-08-10T04:40:07.984542+00:00"}