Analysis approach of relevance trend of time series withmultiple uncertain features
多特征不确定时间序列的关联趋势分析

In view of the deficiency of means to directly calculate the relational degree between time series of uncertain information, this paper proposed the approach of the relevant trend analysis between series of intuitionistic fuzzy sets. The approach, firstly, quantified the difference between uncertain information by using the distance of intuitionistic fuzzy sets. Secondly, using equivalence relation between IFS and internal numbers, calculated the relevant degree of time series, on the basis of definition of distance between time series of interval numbers. Finally, classified the relevance trend of uncertain information series through the method of set pair analysis. The approach expanded the application range of relational degree from crisp numbers to uncertain environment expressed by intuitionistic fuzzy sets, and it classified the degree of relevance trend between time series with multiple uncertain features. Compared with the C-means algorithm and the simulation anneal algorithm, experiment results indicate, by using this method, the accurate rate of the algorithm is higher; and the false alarm rate and the missing alarm rate are both lower; furthermore, it reduces the running time effectively.