基于时态边缘算子的时间序列自主分段表示法

时间序列具有数据量大、维数高和更新速度快等特点，导致一般的分段线性方法难以刻画原始时间序列的全局趋势特征。针对时间序列的特性，提出了一种基于时态边缘算子的自主分段表示方法（简称APLR_TEO），能够有效刻画出时间序列的形状特征。首先通过时态边缘算子与原始时间序列做卷积并根据关联规则得到边缘极值点；然后根据时序的变化特征，采用趋势转折距离的关联规则进行自主线性分段得到关键点，进而用关键点组成的序列来线性近似表示原始时间序列。实验结果表明，APLR_TEO能够有效地刻画序列的形状特征，针对不同规模的数据集具有良好的适应性和稳定性，有效降低了拟合误差。

An autonomous segmental representation of time series based on temporal edge operator

Time series have the characteristics of large amount of data, high dimensionality and fast update speed, which makes it difficult for general piecewise linear algorithms to characterize the global trend of the original time series. Aiming at the characteristics of time series, this paper proposes an autonomous piecewise representation method based on temporal edge operators (APLR_TEO), which can describe the shape features of time series effectively. Firstly, the temporal edge operator is convolved with the original time series and the edge extremum is obtained according to association rules. Then, according to the changing characteristics of the time series, the association rules of trend turning distance are adopted to carry out autonomous linear segmentation and get the key points. Finally, the sequence of key points can represent approximately time series. The experiments show that APLR_TEO can effectively depict shape features of the sequence. This algorithm has good adaptability and stability among data sets of different sizes and effectively reduces the fitting error.