Time Series Clustering Method Based on Principal Component Analysis

Abstract

In terms of existing time series clustering method based on Euclidean distance metric, with the increasing dimension of time series, the time complexity of the algorithm will be increased too; and this method can also lead to incorrect clustering result because of it unable to recognize the abnormal values in time series. Principal component analysis retains large variance and contains more information by linear transformation; it can effectively reduce the dimension of the time series and identify outliers. This paper proposes the idea of time series clustering analysis method based on principal component analysis. Firstly, applying principal component analysis to time series dataset, by way of dimension reduction, obtained the corresponding coefficient matrix and eigenvalues. Secondly, using clustering method based on Euclidean distance on the calculated coefficient matrix, the clustering result of coefficient matrix is consistent with time series dataset. Using simulation data and meteorological data to validate this method, the experimental results show that time complexity of time series clustering method proposed in this paper is significantly better than the algorithm based on Euclidean distance, especially for time series dataset which has linear correlation.