基于滑动平均与分段线性回归的时间序列相似性

针对时间序列相似性度量中欧氏距离对异常数据敏感以及DTW距离算法效率低的问题,提出基于滑动平均与分段线性回归的时间序列相似性方法。首先,使用初始可变滑动平均算法以及分段线性回归对原始时间序列进行数据变换,并将分段线性回归的参数(截距与距离)集作为时间序列的特征,以实现时间序列的特征提取和数据降维；然后,利用动态时间弯曲距离进行距离计算。该方法在时间序列相似性上与DTW算法的性能相近,但是在算法效率上几乎提高了96%。实验结果验证了该方法的有效性与准确性。

Time Series Similarity Based on Moving Average and Piecewise Linear Regression

Aiming at the problems that the Euclidean distance is sensitive to the anomaly data and the efficiency of the DTW distance algorithm is low,a time series similarity method based on the moving average and the piecewise linear regression was proposed.Firstly,the original variable-averaging algorithm and the piecewise linear regression are used to transform the original time series.The parameters of the piecewise linear regression (intercept and distance) are taken as the characteristics of the time series so that the feature extraction of the time series is realized,and the data is dimensioned.Then it calculated distance using the dynamic time bending distance.The performance of the method is similar to that of DTW algorithm,but the proposed method is almost 96% higher in algorithm efficiency.The experimental results verify the effectiveness and accuracy of the method.