Time Series Prediction using Mixtures of Experts

We consider the problem of prediction of stationary time series, using the architecture known as mixtures of experts (MEM). Here we suggest a mixture which blends several autoregressive models. This study focuses on some theoretical foundations of the predic(cid:173) tion problem in this context. More precisely, it is demonstrated that this model is a universal approximator, with respect to learn(cid:173) ing the unknown prediction function . This statement is strength(cid:173) ened as upper bounds on the mean squared error are established.