A Fast Time Series Segmentation and Symbolization Method

As one of the important forms of complex data, time series is a hotspot in data mining area. Sequence pat- tern mining is based on time series symbolization, which segments the time series into sub-series and labels them. But most current time series segmentation algorithms are with large computation complexity, so the paper introduces a sim- ple but high efficiency time series segmentation method based on change point detection. And dynamic time warping (DTW) method is used to compute the distance of the sub-series, later the hierarchical clustering is used to group the sub-series and label them. The experiments show the proposed method is feasible and the results are meaningful.