Multivariate Statistical Analysis for Training Process Optimization in Neural Networks-Based Forecasting Models

Abstract

Data forecasting is very important for electrical analysis development, transport dimensionality, marketing strategies, etc. Hence, low error levels are required. However, in some cases data have dissimilar behaviors that can vary depending on such exogenous variables as the type of day, weather conditions, and geographical area, among others. Commonly, computational intelligence techniques (e.g., artificial neural networks) are used due to their generalization capabilities. In spite of the above, they do not have a unique way to reach optimal performance. For this reason, it is necessary to analyze the data’s behavior and their statistical features in order to identify those significant factors in the training process to guarantee a better performance. In this paper is proposed an experimental method for identifying those significant factors in the forecasting model for time series data and measure their effects on the Akaike information criterion (AIC) and the Mean Absolute Percentage Error (MAPE). Additionally, we seek to establish optimal parameters for the proper selection of the artificial neural network model.