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

Computational intelligence approaches to multiple-step-ahead forecasting rely either on 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 limits 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. This is not guaranteed for instance in direct approaches where predictions for different horizons are performed indepedendently. We discuss here a multi-output extension of conventional local modeling approaches and we present and compare three distinct criteria to perform 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 of the NN5 competition.