Large-Scale Time Series Clustering with k-ARs

Zuogong Yue, Victor Solo

Abstract: Time-series clustering involves grouping homogeneous time series together based on certain similarity measures. The mixture AR model (MxAR) has already been developed for time series clustering, as has an associated EM algorithm. However, this EM clustering algorithm fails to perform satisfactorily in large-scale applications due to its high computational complexity. This paper proposes a new algorithm, k-ARs, which is a limiting version of the existing EM algorithm. It shows remarkably good computational performance when applied to large-scale clustering problems as illustrated on some benchmark simulations motivated by some real applications.