Measuring Linear and Nonlinear Associations

In this study, we propose a new approach based on two nonparametric techniques to capture the linear and nonlinear associations. The singular spectrum analysis technique, which is a powerful method for filtering noisy series, is used as a noise reduction method and mutual information is considered for measuring the level of association. The performance of the proposed approach is assessed using the simulated and real time series.