A Dimensionality Reduction Process to Forecast Events through Stochastic Models

This paper describes a dimensionality reduction process to forecast time series events using stochastic models. As well as the KDD process defines a sequence of common steps to achieve useful information through data mining techniques, we propose a sequence of steps in order to estimate the probability of future events through stochastic modeling. Our process focus on reduce the dimensionality of data, thus reducing the effect of the common problems involved in stochastic modeling, such as the state space explosion and the large modeling efforts to create such models.