Trend information for time series classification

One of most important parts of time series data analysis is to choose the appropriate similarity measurement. Among all similarity measurements, the longest common subsequence is a commonly used and effective method. However, the original method only measures the numerical differences of point-to-point sequences, which neglects the trend of the changing sequence. Therefore, a time series discretization method based on the trend information is proposed and the longest common subsequence is employed to carry out similarity measurements. This method can measure time series trend information well. In addition, it is linearly combined with the point-to-point comparison function. In contrast to well-known measures from the literature, the proposed method can take both the trend information of time series and point-to-point comparison function into consideration. The new similarity measurement is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, a set of experiments have been conducted, testing its effectiveness on 42 real time series. The experimental results show that our method can effectively improve the accuracy rate of time series classification.