Hierarchical Matching for Multivariate Time Series Based on Moving Average
Since the length and the dimension of multivariate time series can be vary large, the similarity calculation is often computationally expensive.This paper proposes the hierarchical algorithm for the multivariate time series matching based on moving average.Firstly, the rough matching based on moving average is performed to select candidate sequences according to the corollary of distance reduction theorem.Then, the second matching is performed on the basis of representing the selected candidate sequences with another mode.Finally, we get similar sequences by calculating the Euclidean distance.The experiment demonstrates that the search space is pruned gradually in a short time, the sequences which similar to the given query sequence are found efficiently, and the global feature and the local feature can be kept at the same time.
Multivariate Time Series Hierarchical Moving Average Feature Points
Yan WANG Qianqian MA Meng HAN
College of Computer and Communication,Lanzhou University of Technology,Lanzhou,China
国内会议
第四届信息电子与计算机工程国际会议(The 4th International Conference on Information ,Electronic and Computer Science)
泰安
英文
133-137
2012-11-24（万方平台首次上网日期，不代表论文的发表时间）