Iterated Time Series Prediction with Ensemble Models

Abstract

We describe the use of ensemble methods to build proper models time series prediction. Our approach extends the classical ensemble methods for neural networks by using several different model architectures. We further suggest an iterated prediction procedure to select the final ensemble members. This is an extension of well know the crossvalidation scheme for model validation