PIMAT

The Covid-19 pandemic is plaguing the world and has preoccupied all segments of society, be it local, continental or worldwide. In this sense, strategies for controlling the spread of the virus and consequently its monitoring are proposed by researchers from all over the world, either through predictive models or even the application of mathematical functions for modeling. Nevertheless, the proposals conveyed in the literature are strongly concerned with variables that are supposed or even predicted due to the observations made by the researchers, leaving the experimental study at the margin of the discussion.

Predicting the future has always been an intriguing issue for human beings. Knowing what will happen moments before your time can help in decision making and impact the functioning of different segments of society. The prediction of exchange rates is a classic, recurring problem with a great scientific appeal (given the complexity and non-linearity of market values) and economic (possibility of monetary gains), especially with the current conjuncture of the world financial market where speculation dominates the market on the global stage.

Several issues involve the composition of the quotation values that will determine the formation of the market’s time series. This information is of great relevance for the implementation of forecasting techniques.

In this sense, among the existing traditional econometric techniques and the more elaborate techniques as with the use of artificial intelligence, they need to be studied and demystified in order to try to guarantee an acceptable minimum of precision when trying to predict the Forex stochastic and non-linear series series.

Based on the hypothesis that it is possible to improve trading decisions in the market, this project aims to investigate issues related to the formation of financial market time series as well as the application of econometric techniques and Artificial Intelligence, to implement the cognitive level of a particular agent specializing in predicting Forex series.