Fuzzy multi-hidden Markov predictor in electric load forecasting

We present two new systems that approximate probability density functions (pdFs) 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 pdfs 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.