Independent component analysis for clustering multivariate time series data

Authors:
Edmond H. C. Wu;Philip L. H. Yu
Affiliations:
Department of Statistics & Actuarial Science, The University of Hong Kong, Hong Kong;Department of Statistics & Actuarial Science, The University of Hong Kong, Hong Kong
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

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.