时间序列数据趋势转折点提取算法

Trend Turning Point Extraction Algorithm for Time Series Data

时间序列数据蕴含趋势信息,可以根据数据的趋势信息提取趋势转折点,达到压缩数据、减少噪声影响的目的。通过分析时间序列数据的趋势信息,提出自适应数据趋势转折点提取算法。该算法不依赖任何先验知识,根据数据本身的趋势特征自动提取趋势转折点,提取信息包括坐标索引和对应数据。UCR时间序列分类数据集与SEEP、CAP和PAA等算法进行对比的实验结果表明,在多种数据情况下,该算法拟合误差和分类错误率更小,平均拟合误差为0.373 6,分类错误率同原始数据的分类错误率相比减少3.39%。

The time series data contains trend information, which can extract the trend turning point according to the trend information of the data, and can achieve the purpose of compressing the data and reducing the influence of noise. By analyzing the trend information of time series data, an adaptive data trend turning point extraction algorithm is proposed. The algorithm does not rely on any prior knowledge, only according to the trend characteristics of the data itself automatically extract the trend turning point, extracted information including the coordinate index and the corresponding data. Compared with SEEP, CAP and PAA algorithm, experimental results show that the fitting error and classification error rate of the algorithm are smaller in the case of multiple data, and the average fitting error is 0. 373 6, the classification error rate compared with the original data classification error rate decreases by 3.39% .