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題名 Biologically Inspired Fuzzy Forecasting: A New Forecasting Methodology 作者 鄭至甫
Jeng, Jyh‐Fu ;J. Watada;B. Wu貢獻者 科管智財所 關鍵詞 Forecasting;Bio-inspired computing;Fuzzy time series forecasting;Nonlinear time series analysis 日期 2009.12 上傳時間 7-Nov-2014 16:02:28 (UTC+8) 摘要 There are many forecasting techniques including the ARIMA model, GARCH model, exponential smoothing, neural networks, genetic algorithm, etc. Those methods, however, have their drawbacks and advantages. Since financial time series may be influ- enced by many factors, such as trading volume, business cycle, oil price, and seasonal factor, conventional model based on prediction methodologies and hard computing meth- ods seem inadequate. In recent years, the innovation and improvement of forecasting methodologies have caught more attention, and also provide indispensable information in the decision-making process, especially in the fields of financial economics and engi- neering management. In this paper, a new forecasting methodology inspired by natural selection is developed. The new forecasting methodology may be of use to a nonlinear time series forecasting. The method combines mathematical, computational, and biological sciences, which includes fuzzy logic, DNA encoding, polymerase chain reaction, and DNA quantification. In the empirical study, currency exchange rate forecasting is demonstrated. The Mean Absolute Forecasting Accuracy method is defined for evaluating the performance, and the result comparing with the ARIMA method is illustrated. 關聯 International Journal of Innovative Computing, Information and Control, 5(12), 4835-4844 資料類型 article dc.contributor 科管智財所 en_US dc.creator (作者) 鄭至甫 zh_TW dc.creator (作者) Jeng, Jyh‐Fu ;J. Watada;B. Wu en_US dc.date (日期) 2009.12 en_US dc.date.accessioned 7-Nov-2014 16:02:28 (UTC+8) - dc.date.available 7-Nov-2014 16:02:28 (UTC+8) - dc.date.issued (上傳時間) 7-Nov-2014 16:02:28 (UTC+8) - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/71237 - dc.description.abstract (摘要) There are many forecasting techniques including the ARIMA model, GARCH model, exponential smoothing, neural networks, genetic algorithm, etc. Those methods, however, have their drawbacks and advantages. Since financial time series may be influ- enced by many factors, such as trading volume, business cycle, oil price, and seasonal factor, conventional model based on prediction methodologies and hard computing meth- ods seem inadequate. In recent years, the innovation and improvement of forecasting methodologies have caught more attention, and also provide indispensable information in the decision-making process, especially in the fields of financial economics and engi- neering management. In this paper, a new forecasting methodology inspired by natural selection is developed. The new forecasting methodology may be of use to a nonlinear time series forecasting. The method combines mathematical, computational, and biological sciences, which includes fuzzy logic, DNA encoding, polymerase chain reaction, and DNA quantification. In the empirical study, currency exchange rate forecasting is demonstrated. The Mean Absolute Forecasting Accuracy method is defined for evaluating the performance, and the result comparing with the ARIMA method is illustrated. en_US dc.format.extent 98 bytes - dc.format.mimetype text/html - dc.language.iso en_US - dc.relation (關聯) International Journal of Innovative Computing, Information and Control, 5(12), 4835-4844 en_US dc.subject (關鍵詞) Forecasting;Bio-inspired computing;Fuzzy time series forecasting;Nonlinear time series analysis en_US dc.title (題名) Biologically Inspired Fuzzy Forecasting: A New Forecasting Methodology en_US dc.type (資料類型) article en
