Appling fuzzy theory in estimation and forecasting for time series with outliers

• Main Authors: Hung-Yuan Chen, 陳鴻元
• Other Authors: Chien-wei Chang
• Format: Others
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/36072910537080871490

碩士 === 大同大學 === 應用數學學系(所) === 99 === 　　In 1982, Tanaka et al. put forth an argument regarding the fuzzy regression model, and he suggested that the residuals between an observed value and an estimated value result from uncertainty of the parameters. Plenty subsequent studies indicate that the fuzzy regression model is conducive to the processing of correlated uncertain data. Considering that time series data are characterized by high self-correlation and uncertainty, it is feasible to apply fuzzy theory in the estimation of parameters of a time series model. 　　Outliers abound in data gathered. The effect of outliers on the estimation of parameters of a model is seldom negligible. Hence, another objective of this study is to look for a robust estimation method. Many researches pointed out that the application of fuzzy theory in parameter estimation is robust and effective in eliminating estimation difficulties which might otherwise arise from fuzziness and uncertainty of data. 　　This study involves applying the concept of fuzzy clustering in the construction of a time series model, treating parameters of model as fuzzy numbers, and estimating the parameters with an estimation algorithm created by fuzzy weighted least squares. 　We also give some simulate and empirical examples to illustrate the techniques and to analyze fuzzy data. Results show that the methods proposed by us are more realistic and reasonable for the time series data with outliers