Classification for Time Series Data Based on Center Sequences of Clusters

Classification algorithm is one of the important tasks and techniques in the field of time series data mining. A classification method for time series data based on center sequences of clusters is proposed in this paper. Time series in the training set are divided into several clusters according to their labels, and every cluster picks out the representation objects using affinity propagation clustering and constructs the representation subset. The barycenter averaging method based on dynamic time warping is used to calculate the center group in which the improved K nearest neighbors method is executed for time series classification. The experimental results demonstrated that the new method, compared to the traditional method, has better classification quality and calculation performance.