Fuzzy multi-hidden Markov predictor in electric load forecasting

We present two new systems that approximate probability density functions (pdf's) in order to predict continuous values of time series: the Fuzzy Multi-Hidden Markov Predictor (FMHMP) and the Multi-Hidden Markov Model for Regression (MHMMR). They use fuzzification or discretization of continuous data and Dynamic Bayesian Networks (DBN's) to estimate pdf's and then make continuous predictions. A DBN is a Bayesian Network that represents a temporal probability model. The employed DBN is a generalization of the Hidden Markov Model that allows multiple hidden variables. The new systems are applied to the task of monthly electric load single-step forecasting and successfully compared with other fuzzy and discrete probabilistic predictors, two Kalman Filter Models, and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing. The employed time series present a sudden significant changing behavior at their last years, as it occurs in an energy rationing.