A preliminary study on time series forecast of fair-weather atmospheric electric field with WT-LSSVM method

Abstract

Accurate forecasting of fair-weather atmospheric electric field is of great importance to denote the climate change, and it also plays a vital role in lightning forecasting. In this paper, the WT (Wavelet transform)-LSSVM (Least squares support vector machine) method is proposed to develop a model to forecast time series of fair-weather atmospheric electric field. The application experiments with data from Verhnee Dubrovo, Dusheti, and Voeikovo stations show that the predicted data and observed value have the same tendency. The correlation coefficients between them are 0.8149, 0.6395 and 0.7308, while the MSE are 16.9869 V/m, 28.8876 V/m and 32.2163 V/m, respectively. Furthermore, comparing to LSSVM and ANN, WT-LSSVM is a superior method to predict the monthly mean values of fair-weather atmospheric electric field. • A new method is proposed to forecast of fair-weather atmospheric electric field. • The model is a running-extrapolation model based on WT -LSSVM method. • Comparing with LSSVM and ANN, WT-LSSVM method is better for prediction.