###### 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. neural networks design; parameter optimization; multivariate statistical analysis +
Footnote †: journal: Medical  
compared with the RBF network for the same estimated variable. In conclusion, they present a new training process (CUDA-RBF-SOM) that reduces the execution time by 99.8846% compared to an RBF-SOM model. In some cases, the quantity of data for the training process can lead to a problem, so the architecture must not be limited by data. The neural network model proposed by [6] is focused on reducing the quantity of input data needed in the training process. An RBF network is applied to regression problems; the new structure consists of a multi-stage training process that matches the orthogonal least squares (OLS) with an optimization gradient. The model validation tests are performed on the data for the prediction of stress and force in the knee required to prevent injuries in different scenarios (tasks), in which the results and estimated times are compared with other models, such as OLS and feed-forward back-propagation network (FFN), with their real values. The proposed structure named Opt-RBN shows, in some scenarios, favorable results in the training data. Although the training process time is a little longer, the authors consider it a good time because it is less than one minute. The Stochastic Gradient Descent (SGD) model during the training process updates a Convolutional Neural Network (CNN) with a noisy gradient calculated from a random batch. Each batch uniformly updates the network every once in a while, which leads to loss problems in the batches. A model to mitigate this problem is proposed in the form of a stochastic process that automatically selects the batch with the largest loss to accelerate its training. This is called Inconsistent Stochastic Gradient Descent (ISGD) by [7]. The key concept is that the inconsistent training dynamically adjusts the training effort without loss. ISGD gradually reduces the average lost batch and uses a dynamic upper control limit to identify a large lost batch as it goes along. The ISGD remains in the identified batch to speed up the training with additional gradient updates. The tests for validating the ISGD are based on data such as those derived from ImageNet (A large-scale hierarchical image database), MNIST (Modified National Institute of Standards and Technology database), and CIFAR-10 (Canadian Institute For Advanced Research database). Those tests showed a convergence of the ISGD that was 14.94% faster than the SGD using the ImageNet database. ISGD showed a convergence that was 23.57% faster than the SGD in the CIFAR-10 test. It also showed a convergence that was 28.57% faster than the SGD using the MNIST database.