Partition of Time Series Data Using Hierarchical Clustering in Online Environment

Abstract

Time series is a sequence of data in a temporal axis. We have proposed a method for partitioning time series data into several periods whose trends are different from the adjacent with a hierarchical clustering. In the real-time environment, we need to do clustering every time when a new data are added. In this paper, we apply the method to an online environment. First, we partition the initial data into several clusters. Then, we determine the first cluster to be a period hereafter the data in the period are not considered for clustering, and store the size of the second cluster as the threshold. Every adding a new data to the time series, we do clustering for the time series data and we determine the first cluster to be a period with updating the threshold if the size of the first cluster is successively greater than the threshold several times. We repeat these processes. This method makes it possible to apply our partition method to an online environment because the number of data is decreased significantly in the clustering.