Time Series Analysis and Prediction Using Gated Experts with Application to Energy Demand Forecasts

In the analysis and prediction of real world systems two of the key problems are nonstation arity (often in the form of switching between regimes) and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts consisting of a nonlinear gating network and several also nonlinear competing experts. Each expert learns to predict the conditional mean and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert given the input. This article focuses on the case where the gating network bases its decision on infor mation from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state s i e on the output of the gating network at the previous time step as well as to averaging over several predictors. In contrast, gated experts soft partition the input space. This article discusses the underlying statistical assumptions, derives the w...