Comparing and Combining MLP and NEAT for Time Series Forecasting

Neural networks are one of the widely-used time series forecasting methods in time series applications. Among different neural network architectures and learning algorithms, the most popular choice is the feedforward Multilayer Perceptron (MLP). However, it suffers from some drawbacks such as getting trapped in local minima, human intervention during the stage of training, and limitations in architecture design. The aims of this study were twofold. The first was to employ NeuroEvolution of Augmenting Topologies (NEAT), which has many successful applications in numerous fields. In this paper, we applied it to time series forecasting for the first time and compared its performance with that of the MLP. The second aim was to analyse the performance resulting from the pairwise combination of these methods. In general, the results suggested that the forecasts from the NEAT algorithm were more accurate than those of the MLP. The results also showed that pairwise combined forecasts in general were better than single forecasts. The best forecasts of all were obtained by pairwise combination of MLP and NEAT