Fuzzy Bayes and Fuzzy Markov Predictors
Marcelo Teixeira, Gerson Zaverucha · Journal of Intelligent & Fuzzy Systems · 2002
Two fuzzy probabilistic predictors, the Fuzzy Bayes Predictor (FBP) and the Fuzzy Markov Predictor (FMP), are described and evaluated for the task of single-step monthly electric load forecasting. They are modifications of the Naive Bayes Classifier and Hidden Markov Model (where temporal dependence among the examples is ignored and taken into account, respectively) to enable them to predict numerical values by incorporating features from fuzzy systems. This is a generalization of the regression-by-discretization approach of using classifiers for regression. These hybrid systems are successfully compared with two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.