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Long-Term Prediction of Time Series by combining Direct and MIMO Strategies
Souhaib Ben Taieb, Gianluca Bontempi, Antti Sorjamaa and Amaury Lendasse
In: IJCNN2009, June 14-19, Atlanta, USA.
AbstractReliable 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 strategy
and discusses their respective limitations to the problem of longterm
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. [Edit]
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