Evaluating probabilistic graphical models for forecasting

Forecasting represents a very important task for control and decision making in many fields. Forecasting the dollar price is important for global companies to plan their investments. Forecasting the wind speed for a day-ahead horizon allows dispatching clean energy efficiently. One technique developed by the artificial intelligence community that has proved to be efficient for forecasting is the probabilistic graphical models approach. In order to obtain accurate models for forecasting, there exist different assumptions that might be made. This paper presents these assumptions and the results of different experiments conducted to define the characteristics of good probabilistic graphical models. A performance comparison of several graphical models is also presented. The experiments were executed to forecast wind velocity and hence, wind power in wind farms.