Partial mutual information for input selection of time series prediction
Conggui Yuan, Xinzheng Zhang, Shuqiong Xu · 2011
An important step in modeling time series is the selection of appropriate model input. Information theoretic concept of mutual information provides a general framework to evaluate the dependence between a potential model input and the output. A model-free approach, partial measure of the mutual information, is proposed in this paper, which utilizes a measure of the mutual information criterion to characterize the dependence in the case of multiple inputs and identifies the actual inputs for time series prediction. This algorithm is tested on a number of synthetic time series data sets, where the dependence attributes were known a priori. Results depict the effectiveness of the proposed method in proper input selection.