Independent Component Analysis (ICA) is a useful statistical method for separating mixed data sources into statistically independent patterns. In this paper, we apply ICA to transform multivariate time series data into independent components (ICs), and then propose a clustering algorithm called ICACLUS to group underlying data series according to the ICs found. This clustering algorithm can be used to identify stocks with similar stock price movement. The experiments show that this method is effective and efficient, which also outperforms other comparable clustering methods, such as K-means. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Wu, E. H. C., & Yu, P. L. H. (2005). Independent component analysis for clustering multivariate time series data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3584 LNAI, pp. 474–482). Springer Verlag. https://doi.org/10.1007/11527503_57
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