Clustering Method for Financial Time Series with Co-Movement Relationship

Due to the random walk property of the financial time series, it is very difficult to develop a system that solves real financial application problems. However, if we obtain a time series cluster with a high degree of co-movement, it will be very useful for developing financial application systems. This paper proposes a clustering method that finds time series clusters with higher degrees of co-movement than the existing time series clustering algorithms. There is a problem in that clusters generated by the existing time series clustering algorithms contain too much noise with a low degree of co-movement. We propose a clustering method that solves the problem. This method is performed in the following steps. In the Data Preprocessing step, it performs Average Scaling, Weighted Time Series Transformation, Dimension Reduction, and Cluster Diameter Estimation. In the Clustering Step, it performs Preclustering and Refinement. Experiments show that our clustering method has higher performance than the existing time series clustering algorithms in finding clusters with high degree of co-movement.