Abstract

In this paper, we propose a new approach based on two nonparametric techniques to capture 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 simulated and real time series.

Keywords:

Association, mutual information, noise reduction, singular spectrum analysis,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.