Overview

Human beings during their existence have sought to infer events and occurrences through learning based on the experience acquired day by day; in this sense we can say that the forecast, prediction and projection is an inherent practice in humans that arises from an empirical or scientific knowledge, for example, a person observing the airspace, the color of the clouds and the wind can infer whether it will rain or not, a meteorologist based on atmospheric variables can forecast the weather for the coming seasons. The statistical regression models are part of some of the most common prediction and projection methods, however there are some difficulties in them, the requirements for their application are numerous, among them we can mention the independence of the data, the normality and equality of variance, the linearity, the normality of the residuals and the master size. Faced with this difficulty we can make use of artificial neural networks (ANN) as an alternative solution, ANNs have been conceptualized as nonparametric statistical techniques.

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