# Artificial Intelligence

Artificial intelligence is one of the most vital scientific areas and is a constantly innovative industry. MatDeck has countless artificial intelligence algorithms implemented in two vast areas, at this moment in time. One area being time series processing with a heavy focus on forecasting future values of economic time series, and the second area is based on classification and machine learning.

The traditional approaches to time series predictions, specifically for economic series, are Box-Jenkins or ARIMA methods. The ARIMA model uses an iterative three-stage modelling approach which consists of: model identification and selection, parameter estimation and model realisation. In MatDeck, autocorrelation and partial autocorrelation function – pacf() are used for model identification. For parameter estimation, maximum likelihood estimation computation algorithms are used to arrive at coefficients, for instance Yule-Walker estimation – yulewalker() is used to estimate AR parameters. Forecasting is implemented using appropriate models with estimated parameters – arforecasting(). The quality of predictive capabilities in MatDeck can be tested and evaluated using various statistical error functions such as mean square error – mse(), mean percentage error – mpe() etc.

The Naive Bayes algorithm is one of the very first and effective methods used for classification and machine learning which is implemented in MatDeck. This algorithm is a clever method that makes an informed decision regarding the classification of something by using the probabilities of all the attributes belonging to each class. It greatly simplifies the process of calculating the probabilities of each attribute by assuming that the probability of each attribute in each class is independent of all other attributes. In MatDeck, the implementation of Naive Bayes consists of two functions. The first function, naivebayest(), is used for training and prepares that data in a format to be used in the Naive Bayes classification function. The second function, naivebayesc(), is used to calculate the posterior probabilities based on the training data to make an informed decision and then classify a record defined by the attributes.

These are two examples that illustrate time-series forecasting in MatDeck. For illustration, stock price values are predicted based on past values.

There are two key examples that illustrate how the naive Bayes classifier can be utilized in MatDeck.