The multi-dimensional characteristics and high noise characteristics of the financial data set make it hard to analyze the time series. This paper puts forward an algorithm based on the principal component analysis and the Eros affinity propagation clustering. First it uses the principal component analysis method to extract the main eigenvalues of the multivariate financial time series data; then uses the Eros affinity propagation clustering to analyze the extracted eigenvalues. This kind of clustering algorithm can make the individual data as an attribute of the original data, through iterate competition to achieve optimal, do not need to find the number of clusters. The research results show that, this integrated method greatly reduces the dimension of the time series, and has a highly correct classification rate. It proves that this algorithm is very effective.
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