Dissimilarity Measures for Time Series and Trend Analysis: Application to COVID-19 Cases Series  

作　　者：Norio Watanabe 

机构地区：[1]Department of Industrial and Systems Engineering,Chuo University,Japan

出　　处：《Journal of Mathematics and System Science》2021年第1期1-12,共12页数学和系统科学（英文版）

摘　　要：In this paper we propose some dissimilarity measure functions for trends of nonstationary time series.If time series are stationary,the cross correlation function can be applied as a similarity measure.However,the validity of the cross correlation function is lost for nonstationary time series.Moreover,the cross correlation function cannot be calculated if one of trends is constant.The proposed functions can be applied even if trends are constant and their values are determined through the minimization.The clustering is considered as an application of the dissimilarity measure.Furthermore the problem of the common trend within multiple time series is considered and the estimation algorithm is proposed.Usability of the proposed method is demonstrated by applying to series of COVID-19 cases in Japan.

关 键 词：Cross correlation function CLUSTERING common trend. 

分 类 号：O17[理学—数学]