Time Series Classification by Modeling the Principal Shapes

Time series classification has been attracting significant interests with many challenging applications in the research community. In this work, we present a novel time series classification method based on the statistical information of each time series class, called Principal Shape Model (PSM), which can quickly and effectively classify the time series even if they are very long and the dataset is very large. In PSM, the time series with the same class label in the training set are gathered to extract the principal shapes which will be used to generate the classification model. For each test sample, by comparing the minimum distance between this sample and each generated model, we can predict its label. Meanwhile, through the principal shapes, we can get the intrinsic shape variation of time series of the same class. Extensive experimental results show that PSM is orders of magnitudes faster than the state-of-art time series classification methods while achieving comparable or even better classification accuracy over common used and large datasets.