Analysis of regional climate variables by using neural Granger causality

In recent years, how to discover causality rather than correlation among climate variables and how to use causality to help in time-series tasks have received great concern. However, the high dimensionality and nonlinearity of climate variables are the main issues for causal inference based on historical large-scale climate time series. Therefore, a method based on neural Granger causality inference is proposed to study the interactions of climate variables, with focus on the variables commonly used in the energy field, especially in photovoltaics. Firstly, for each climate variable, the time-varying causality and global causality are, respectively, obtained on each time window and on the whole series by neural Granger causality inference. Secondly, the global causality is used as a feature selection map of the input variables in the prediction task. Finally, compared with some existing feature selection methods, the experiments determine that the proposed method not only reveals the appropriate causality rather than the correlation among climate variables, but also efficiently reduces the input dimensionality and improves the performance and interpretability of the predicting model.