关键词: 多元时间序列, 特征降维, 共同核主成分, 角度优化, 噪声, 云计算平台

Abstract: Multivariate Time Series（MTS） is featured as high noises, nonlinear and mess. However, the traditional method based on distance to reduce dimension has difficulty in dealing with the subspace deviation which is caused by noises and the dramatic increase of data. In this essay, a new analysis is proposed based on Angle Optimized Global Embedding（AOGE） and Principal Component Analysis（PCA）. This new analysis method is equipped on Hadoop platform for improved parallelization which effectively deals with the subspace deviation caused by noises and the calculation problem caused by massive data. Through experiment, the new method has proved its effectiveness, operating efficiency and flexibility, showing that this method can effectively reduce dimension of MTS with noises. The parallelized method which is bases on the Hadoop platform has good efficiency and flexibility.

Key words: Multivariate Time Series（MTS）, feature dimension reduction, common kernel principal component, angle optimized, noise, cloud computing platform