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.
Highlights
Neural network design requires the proper determination of the input variables, i.e., the appropriate selection of the factors that affect the variable behavior to be modeled
There are no handled performance metrics for unsupervised neural networks to ensure that the configuration developed has reached the optimal performance [1]
Since the objective of this study is to identify the significant factors in the performance of a neural network for forecasting purposes, we chose to select as response variables the Akaike information criterion (AIC) and the Mean Absolute Percentage Error (MAPE)
Summary
Neural network design requires the proper determination of the input variables, i.e., the appropriate selection of the factors that affect the variable behavior to be modeled This is not a trivial issue because there is no formal theory to ensure that the selected network is the best for a particular application problem. The state-of-the-art reports that the Akaike information criterion (AIC) is proposed as a measure of comparison to identify a suitable configuration [2,3]. For these reasons, it is necessary to know which factors influence the behavior of these two-performance metrics, so that it can be established whether it is possible to determine an optimal operating point for AIC and an appropriate dispersion index level for MAPE
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