Prediction of Sunspot Series Using BiLinear Recurrent Neural Network

Abstract

A prediction scheme of sunspot series using a BiLinear Recurrent Neural Network (BLRNN) is proposed in this paper. Since the BLRNN is based on the bilinear polynomial, it has been successfully used in modeling highly nonlinear systems with time-series characteristics and the BLRNN can be a natural choice in predicting sunspot series. The performance of the proposed BLRNN-based predictor is evaluated and compared with the conventional MultiLayerPerceptron Type Neural Network (MLPNN)-based predictor. Experiments are conducted on the Wolf sunspot series number data. The results show that the proposed BLRNNbased predictor outperforms the MLPNN-based one in terms of the Normalized Mean Squared Error (NMSE).

Trend estimation and univariate forecast of the sunspot numbers: Development and comparison of ARMA, ARIMA and Autoregressive Neural Network models

“…Conway et al (1998) examined the use of feed forward neural networks in the long-term prediction of SNs. Park and Woo (2009) compared the performance of two types of ANN in forecasting the monthly SNs.…”