关键词: 时间序列, 重要点, 分段线性表示, 拟合误差

Abstract: The time series linear segmentation algorithm based on important points can preserve the global characteristics of time series and the high precision of fitting. The traditional time series segmentation algorithm based on important points needs to specify the parameters such as the error threshold. These parameters are related to the original data, which is not convenient for the user, and the fitting effect needs to be further improved. In order to solve this problem, this paper proposes a  segmentation algorithm, PLR_TSIP, which is based on the important point of the time series. The method first takes into account the size of the overall fitting error and the length of the sequence, followed by pre-segmentation for the higher priority segment to find the optimal segment. Finally, taking into account the segmentation of the maximum point and the minimum point of the same relationship in the segmentation, which can be a number of important points of the division. Compared with the traditional segmentation algorithm, the fitting error is reduced by the experimental analysis of multiple data sets, and the fitting effect is improved. Compared with the important point segmentation algorithm, at the same time, the efficiency of the segmentation is improved.

Key words: time series, important point, piecewise linear representation, fitting error