  Accurate time series prediction over long future horizons is challenging and
of great interest to both practitioners and academics. As a well-known
intelligent algorithm, the standard formulation of Support Vector Regression
(SVR) could be taken for multi-step-ahead time series prediction, only relying
either on iterated strategy or direct strategy. This study proposes a novel
multiple-step-ahead time series prediction approach which employs
multiple-output support vector regression (M-SVR) with multiple-input
multiple-output (MIMO) prediction strategy. In addition, the rank of three
leading prediction strategies with SVR is comparatively examined, providing
practical implications on the selection of the prediction strategy for
multi-step-ahead forecasting while taking SVR as modeling technique. The
proposed approach is validated with the simulated and real datasets. The
quantitative and comprehensive assessments are performed on the basis of the
prediction accuracy and computational cost. The results indicate that: 1) the
M-SVR using MIMO strategy achieves the best accurate forecasts with accredited
computational load, 2) the standard SVR using direct strategy achieves the
second best accurate forecasts, but with the most expensive computational cost,
and 3) the standard SVR using iterated strategy is the worst in terms of
prediction accuracy, but with the least computational cost.
