基于特征点界标过滤的时间序列模式匹配方法.

Dynamic time warping distance can measure unequal time series and has high matching accuracy, so time series pattern matching can use it. However, its high computational complexity restricts its application in large-scale data sets. Inorder to balance the measurement results and computational complexity of time series pattern matching, this paper pointed out a time series pattern matching method based on feature point landmark filtering. Firstly, it proposed a feature extraction method for feature point landmark filtering, which retained the main features of the time series and compressed the time dimension. Then, feature sequence used dynamic time warping distance for similarity measurement. Finally, the method was validated on the application data set. Experimental results show that the method can effectively reduce the computational complexity while ensuring high accuracy. [ABSTRACT FROM AUTHOR]