题名

區間時間數列預測及其效率評估

并列篇名

Interval Time Series Forecasting and Efficiency Evaluation

DOI

10.6704/JMSSD.2008.5.4.1

作者

洪錦峰(Feng-Hung Cheng);吳柏林(Ber-Lin wu)

关键词

區間時間數列 ; 天氣預測 ; 區間預測 ; 效率評估 ; The interval time series ; the weather forecasting ; the interval forecasting ; efficiency evaluation

期刊名称

管理科學與統計決策

卷期/出版年月

5卷4期(2008 / 12 / 01)

页次

1 - 13

内容语文

繁體中文

中文摘要

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。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。

英文摘要

The point forecasting stated for present use most forecasts, its efficiency evaluation also many by least squares and error (minimum of sum of square errors) primarily. Either the month economy or the wealth gold target forecasting is every day the point forecasting the most common example. But along with interval time series real demand and soft computation (soft computing) technical development, the interval computation and the forecasting receive more and more take seriously. This article proposed that several interval time series forecasting's method, and studies its efficiency evaluation. Finally we affect the industrial crop the weather forecasting, makes the empirical study analysis. The consideration under the non-parameter condition, several forecasting techniques makes the efficiency evaluation and the accurate discussion. The weather forecasting is the f interval forecasting example, establishes the appropriate interval forecasting technique and the efficiency evaluation, will have the greatest help to each research area.

主题分类 基礎與應用科學 > 統計
社會科學 > 管理學
参考文献
  1. S.K. Chang(2007).On the Testing Hypotheses of Mean and Variance for Interval Data.Management Science & Statistical Decision,4(2),63-69.
    連結:
  2. C. Chatfield(1993).Calculating Interval Forecasts.Journal and Business & Economic Statistics,11(2)
  3. Christoph Römer,Abraham Kandel(2000).Statistical tests for fuzzy data.Fuzzy sets and systems,72(1),1-26.
  4. Fang-Mei Tseng,Gwo-Hshiung Tzeng(2002).A fuzzy seasonal ARIMA model for forecasting.Fuzzy sets and systems,126(3),367-376.
  5. Fang-Mei Tseng,Gwo-Hshiung Tzeng,Hsiao-Cheng Yu,Benjamin J. C. Yuan(2001).Fuzzy ARIMA model for forecasting the foreign exchange market.Fuzzy sets and systems,118(1),9-19.
  6. H. L. Hsu(2008).Evaluating forecasting performance for interval data.Computers and Mathematics with Applications,56,2155-2163.
  7. Hung T. Nguyen,Berlin Wu(2006).Fundamentals of Statistics with Fuzzy Data.New York:Springer.
  8. Kunhuang Huarng(2001).Effective lengths of intervals to improve forecasting in fuzzy time series.Fuzzy sets and systems,123(3),387-394.
  9. M. Khashei,S.R. Hejazi,M. Bijari(2008).A new hybrid artificial neural networks and fuzzy regression model for time series forecasting.Fuzzy sets and systems,159,769-786.
  10. S. M. Chen(1996).Forecasting enrollments based on fuzzy time series.Fuzzy sets and systems,81,311-319.
  11. T. B. Ludermir(2008).Forecasting models for interval-valued time series.Neurocomputing,71,3344-3352.
  12. V. Kreinovich,H. T. Nguyen,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.
  13. 吳柏林(1995)。時間數列分析導論。臺北:華泰書局。
  14. 吳柏林(2005)。模糊統計導論-方法與應用。臺北:五南書局。
  15. 張曙光(2007)。國立政治大學應用數學系。