Optimal Input Variables Disposition of Artificial Neural Networks Models for Enhancing Time Series Forecasting Accuracy

Artificial Neural Networks (ANNs) models play an increasingly significant role in accurate time series prediction tools. However, an accurate time series forecasting using ANN requires an optimal model. Hence, great forecasting methods have been developed from optimized ANN models. Most of them focus more on input variables selection and preprocessing, topologies selection, optimum configuration and its associated parameters regardless of their input variables disposition. This paper provides an investigation of the effects of input variables disposition on ANNs models on training and forecasting performances. After investigation, a new ANNs optimization approach is proposed, consisting of finding optimal input variables disposition from the possible combinations. Therefore, a modified Back-Propagation neural networks training algorithm is presented in this paper. This proposed approach is applied to optimize the feed-forward and recurrent neural networks architectures; both built using traditional techniques, and pursuing to forecast the wind speed. Furthermore, the proposed approach is tested in a collaborative optimization method with single-objective optimization technique. Thus, Genetic Algorithm Back-Propagation neural networks aim to improve the forecasting accuracy relative to traditional methods was proposed. The experiment results demonstrate the requirement to take into consideration the input variables disposition to build a more optimal ANN model. They reveal that each proposed model is superior to its old considered model in terms of forecasting accuracy and thus show that the proposed optimization approach can be useful for time series forecasting accuracy improvement.