Long-term prediction of time series by combining direct and MIMO strategies

Authors:
Souhaib Ben Taieb;Gianluca Bontempi;Antti Sorjamaa;Amaury Lendasse
Affiliations:
Computer Science Department, Faculty of Sciences, Université Libre de Bruxelles;Computer Science Department, Faculty of Sciences, Université Libre de Bruxelles;Department of Information and Computer Science, Helsinki University of Technology, Finland;Department of Information and Computer Science, Helsinki University of Technology, Finland
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

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.