Conditionally dependent strategies for multiple-step-ahead prediction in local learning

Computational intelligence approaches to multiple-step-ahead forecasting rely on either iterated one-step-ahead predictors or direct predictors. In both cases the predictions are obtained by means of multi-input single-output modeling techniques. This paper discusses the limitations of single-output approaches when the predictor is expected to return a long series of future values, and presents a multi-output approach to long term prediction. The motivation for this work is that, when predicting multiple steps ahead, the forecasted sequence should preserve the stochastic properties of the training series. However, this may not be the case, for instance in direct approaches where predictions for different horizons are produced independently. We discuss here a multi-output extension of conventional local modeling approaches, and present and compare three distinct criteria for performing conditionally dependent model selection. In order to assess the effectiveness of the different selection strategies, we carry out an extensive experimental session based on the 111 series in the NN5 competition.