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 Title: 區間預測及其效率評估Interval Forecasting with Efficiency Evaluation Authors: 洪錦峰Hung,Chin Feng Contributors: 吳柏林Wu,Berlin洪錦峰Hung,Chin Feng Keywords: 區間時間數列天氣預測區間預測效率評估The interval time seriesthe weather forecastingthe interval forecastingefficiency evaluation Date: 2008 Issue Date: 2009-09-18 18:28:52 (UTC+8) Abstract: 點預測為目前使用最多之預測陳述,其效率評估亦多以最小平方和誤差(minimum of sum of square errors)為主。每日或月的經濟或財金指標預測是點預測最常見的例子。但是隨著區間時間數列真正需求與軟計算(soft computing)科技的發展,區間計算與預測愈來愈受重視。本文提出幾種區間時間數列預測的方法,並研究其效率評估。在第三章,我們定義區間誤差和,並將其對應到實數值,以便用傳統的方法計算。最後我們以影響經濟作物的天氣預測,作實證研究分析。考慮在無參數條件下,幾種預測方法作效率評估與準確性探討。天氣預測是區間預測的例子,建立合適的的區間預測方法與效率評估,對各研究領域將會有莫大的幫助。Currently, the most use of forecasts is the point forecasting, whose efficiency evaluations are major in the least squares and error (minimum of sum of square errors). The common examples of the point forecasting are daily or monthly economy index or financial estimation. But along with the real demand of interval time series and the development of soft computation (soft computing), the interval computation and the forecasting are more and more important. This article provides some interval time series forecasting methods, and studies the efficiency evaluation. In chapter 3, we define sum errors of interval and correspond them to the real numbers, so as to compute with traditional way. Finally, we decide to use the weather forecasting which can affect the cash crop to be the empirical study analysis. Consider some forecasting methods under the non-parameter condition to be the efficiency evaluations and the accurate discussion. The weather forecasting is an example of interval forecasting. It will be more helpful of each research area if we establish the appropriate interval forecasting method and the efficiency evaluation.Interval Forecasting with Efficiency Evaluation 1 區間預測及其效率評估 2 1. 前言 4 2. 區間時間數列分析 5 2.1區間模糊數的定義及其性質 5 2.2 區間時間數列的預測模式 7 3. 區間預測之效率分析 8 3.1 區間預測的效率性 8 4. 模式分析與討論 11 4.1 區間時間數列模式建構 11 4.2 預測的方法 12 4.3預測方法的效率評估 13 5. 實證分析 17 6. 區間效率評估的一些性質 25 7. 結論 29 8. 參考文獻 30 Reference: 中文部分：[1]. 吳柏林（1995）時間數列分析導論。台北：華泰書局[2]. 吳柏林（2005）模糊統計導論-方法與應用。台北：五南書局[3]. 張曙光（2007）模糊期望值與模糊變異數的檢定方法。國立政治大學博士論文。英文部分：[1]. H. T. Nguyen and B. Wu (2006) Fundamentals of Statistics with Fuzzy Data. New York：Springer.[2]. S. M. Chen (1996) Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems, 81, 311-319.[3]. T. B. Ludermir (2008) Forecasting models for interval-valued time series. Neurocomputing, 71, 3344-3352.[4]. H. L. Hsu (2008) Evaluating forecasting performance for interval data. Computers and Mathematics with Applications, 56, 2155-2163.[5]. V. Kreinovich , H. T. Nguyen and B. Wu (2007) On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems. Information Sciences, 177, 3228-3238.[6]. C. Chatfield (1993) Calculating Interval Forecasts. Journal and Business ＆ Economic Statistics, 11(2), 121-135.[7]. M. Khashei, S.R. Hejazi and M. Bijari (2008) A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems, 159, 769-786.[8]. C. Römer and A . Kandel (2000) Statistical tests for fuzzy data. Fuzzy sets and systems, 72(1), 1-26.[9]. S.K. Chang (2007) On the Testing Hypotheses of Mean and Variance for Interval Data. Management Science ＆ Statistical Decision, 4(2), 63-69.[10]. F.-M. Tseng and G.-H. Tzeng (2002) A fuzzy seasonal ARIMA model for forecasting. Fuzzy sets and systems, 126(3), 367-376.[11]. K. Huarng (2001) Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy sets and systems, 123(3), 387-394.[12]. F.-M. Tseng, G.-H. Tzeng, H.-C. Yu, and B. J. C. Yuan (2001) Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy sets and systems, 118(1), 9-19. Description: 碩士國立政治大學應用數學研究所9575101597 Source URI: http://thesis.lib.nccu.edu.tw/record/#G0095751015 Data Type: thesis Appears in Collections: [Department of Mathematical Sciences] Theses

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