Real-time Forecasting of Non-linear Competing Online Activities

• Item type: Trans(1)
• 公開日: 2020-04-16
• タイトル: Real-time Forecasting of Non-linear Competing Online Activities
• タイトル / 言語: en
• タイトル: Real-time Forecasting of Non-linear Competing Online Activities
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [研究論文] time-series, non-linear, real-time forecasting
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Kumamoto University, Graduate School of Science and Technology
• 著者所属: Osaka University, The Institute of Scientific and Industrial Research
• 著者所属: Osaka University, The Institute of Scientific and Industrial Research
• 著者所属(英): en
• 著者所属(英): Kumamoto University, Graduate School of Science and Technology
• 著者所属(英): en
• 著者所属(英): Osaka University, The Institute of Scientific and Industrial Research
• 著者所属(英): en
• 著者所属(英): Osaka University, The Institute of Scientific and Industrial Research
• 著者名: Thinh, Minh Do; Yasuko, Matsubara; Yasushi, Sakurai
• 著者名(英): Thinh, Minh Do; Yasuko, Matsubara; Yasushi, Sakurai
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.28(2020) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.28(2020) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 13, 号 2, 発行日 2020-04-16
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会