模糊統計在時間數列分析與相似度之應用

Abstract

在時間數列的分析上，由於一些辨識模型結構的方法，常受制於時間數列本身的非定態及不確定干擾的影響，因此若以單一模式來配適數列往往不能得到滿意的結果。

An important problem in pattern recognition of a time series is similarity recognition. This paper presents the methods of similarity calculation for two time series. The methods considered include equally divided range method, K-rneans method and rank transformed method. The success of our similarity recognition relies a large extent on the fuzzy statistical concept. Simulation results demonstrate that, overall, the equally divided range method performed best in the similarity recognition. While other methods provide superior efficiency in calculating similarity for certain special time series. Finally two empirical examples, similarity calculating about GDP vs. Consumption and GDP vs. Invest, are illustrated.