{"created":"2025-01-19T01:06:39.610783+00:00","updated":"2025-01-19T20:13:20.683291+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00204372","sets":["934:1022:9995:10177"]},"path":["10177"],"owner":"44499","recid":"204372","title":["Real-time Forecasting of Non-linear Competing Online Activities"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-04-16"},"_buckets":{"deposit":"ac7377f4-267d-4c58-9625-103fd61a104e"},"_deposit":{"id":"204372","pid":{"type":"depid","value":"204372","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Real-time Forecasting of Non-linear Competing Online Activities","author_link":["506086","506084","506087","506083","506085","506088"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Real-time Forecasting of Non-linear Competing Online Activities"},{"subitem_title":"Real-time Forecasting of Non-linear Competing Online Activities","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[研究論文] time-series, non-linear, real-time forecasting","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2020-04-16","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Kumamoto University, Graduate School of Science and Technology"},{"subitem_text_value":"Osaka University, The Institute of Scientific and Industrial Research"},{"subitem_text_value":"Osaka University, The Institute of Scientific and Industrial Research"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kumamoto University, Graduate School of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Osaka University, The Institute of Scientific and Industrial Research","subitem_text_language":"en"},{"subitem_text_value":"Osaka University, The Institute of Scientific and Industrial Research","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/204372/files/IPSJ-TOD1302005.pdf","label":"IPSJ-TOD1302005.pdf"},"date":[{"dateType":"Available","dateValue":"2022-04-16"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOD1302005.pdf","filesize":[{"value":"2.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"39"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"8ea969f0-8ffb-426e-89d3-4ee7f7c0b009","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Thinh, Minh Do"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasuko, Matsubara"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasushi, Sakurai"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Thinh, Minh Do","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasuko, Matsubara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasushi, Sakurai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464847","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":"1882-7799","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Given a large, online stream of multiple co-evolving online activities, such as Google search queries, which consist of d keywords/activities for l locations of duration n, how can we analyze temporal patterns and relationships among all these activities? How do we go about capturing non-linear evolutions and forecasting long-term future patterns? For example, assume that we have the online search volume for multiple keywords, e.g., “HTML/Java/SQL/HTML5” or “Iphone/Samsung Galaxy/Nexus/HTC” for 236 countries/territories, from 2004 to 2015. Our goal is to capture important patterns and rules, to find the answer for the following issues: (a) Are there any periodical/seasonal activities? (b) How can we automatically and incrementally detect the sign of competition between two different keywords from the data streams? (c) Can we achieve a real-time snapshot of the stream and forecast long-range future dynamics in both global and local level? In this paper, we present RFCAST, a unifying adaptive non-linear method for forecasting future patterns of co-evolving data streams. Extensive experiments on real datasets show that RFCAST does indeed perform long-range forecasts and it surpasses other state-of-the-art forecasting tools in terms of accuracy and execution speed.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.28(2020) (online)\n------------------------------","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Given a large, online stream of multiple co-evolving online activities, such as Google search queries, which consist of d keywords/activities for l locations of duration n, how can we analyze temporal patterns and relationships among all these activities? How do we go about capturing non-linear evolutions and forecasting long-term future patterns? For example, assume that we have the online search volume for multiple keywords, e.g., “HTML/Java/SQL/HTML5” or “Iphone/Samsung Galaxy/Nexus/HTC” for 236 countries/territories, from 2004 to 2015. Our goal is to capture important patterns and rules, to find the answer for the following issues: (a) Are there any periodical/seasonal activities? (b) How can we automatically and incrementally detect the sign of competition between two different keywords from the data streams? (c) Can we achieve a real-time snapshot of the stream and forecast long-range future dynamics in both global and local level? In this paper, we present RFCAST, a unifying adaptive non-linear method for forecasting future patterns of co-evolving data streams. Extensive experiments on real datasets show that RFCAST does indeed perform long-range forecasts and it surpasses other state-of-the-art forecasting tools in terms of accuracy and execution speed.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.28(2020) (online)\n------------------------------","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌データベース（TOD）"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2020-04-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"13"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":204372,"links":{}}