Characteristic Representation of Stock Time Series Based on Trend Feature Points

Stocks are the most active part of the securities market, and the analysis of stock generally starts from the price fluctuation. Stock trading data have the characteristics of time series, which make it possible to record the transaction prices in a time-evolving order. Due to the large data and high research complexity of time series, some ideal data are difficult to obtain for analyzing and predicting stock movements. Aiming at the problem, we utilize the piecewise linear representation which combines turning points and maximum absolute deviation points in stock time series to extract sequence features. Firstly, the proposed method finds turning points that satisfy the condition given in this paper, and defines the point distance formula to calculate the fitting errors between the subsequence segment and the fitted straight line, whose average value is set as the threshold P and the subsequence length is as the threshold d. Secondly, if the fitting error or the length of the subsequence segment is greater than P or d, respectively, the maximum absolute deviation point is obtained according to the difference between the fitted value and the original data. Finally, all trend feature points are connected by linear interpolation. In this paper, different sequence lengths, different thresholds d, different methods and industrial data from different fields are discussed and compared in detail to verify the proposed method. The experimental results show that the proposed method gets satisfactory data fitting and expanding effect, and retains the characteristics and integrity of the initial time series.