System Identification and Data-Driven Forecasting of AE Index and Prediction Uncertainty Analysis Using a New Cloud-NARX Model

Abstract

Severe geomagnetic storms caused by the solar wind disturbances have harmful influences on the operation of modern equipment and systems. The modeling and forecasting of AE index are extremely useful to understand the geomagnetic substorms. This study presents a novel cloud-nonlinear autoregressive with exogenous input (NARX) model to predict AE index 1 hr ahead. The cloud-NARX model provides AE index forecasting results, with a correlation coefficient of 0.87 on the data of whole year 2015. The benchmarks on the data of the two interested periods of 17–21 March 2015 and 22–26 June 2015 are presented. The presented model uses uncertainty “cloud” model and cloud transformation to quantify the uncertainty throughout the structure detection, parameter estimation, and model prediction. The new predicted band can be generated to forecast AE index with confidence interval. The proposed method provides a new way to evaluate the model based on uncertainty analysis, revealing the reliability of model, and visualize the bias of model prediction.