A Feature Segment Based Time Series Classification Algorithm

Traditional works on time series classification usually use all of data in time series without distinction. However, that will swamp the discriminative information and decrease the correctness of classification. In this paper, a feature segment based time series classification algorithm was proposed, which only selects some highly discriminative time series data for classification. Firstly, an adaptive time series segmentation method was proposed. Then, a large margin based feature segment selection method was given. Based on these two methods, a time series classification framework was established after representing the time series with the optimal segments. By exploring the discriminative temporal patterns hidden in subsequences of time series and giving them more emphasize, the algorithm proposed in this paper can improve the time series classification performance greatly. Extensive experimental results showed that the proposed algorithm can achieve a good classification performance.