Long-Term Prediction of Time Series by combining Direct and MIMO Strategies

Souhaib Ben Taieb, Gianluca Bontempi, Antti Sorjamaa and Amaury Lendasse
(2009) IEEE International Joint Conference on Neural Networks (IJCNN), 2009.


Reliable and accurate prediction of time series over large future horizons has become the new frontier of the forecasting discipline. Current approaches to long-term time series forecasting rely either on iterated predictors, direct predictors or, more recently, on the multi-input multi-output (MIMO) predictors. The iterated approach suffers from the accumulation of errors, the Direct strategy makes a conditional independence assumption, which does not necessarily preserve the stochastic properties of the time series, while the MIMO technique is limited by the reduced flexibility of the predictor. The paper compares the direct and MIMO strategies and discusses their respective limitations to the problem of long-term time series prediction. It also proposes a new methodology that is a sort of intermediate way between the Direct and the MIMO technique. The paper presents the results obtained with the ESTSP 2007 competition dataset.