Multi-step-ahead forecasting using kernel adaptive filtering

Garcia Vega, Sergio [UCL] Castellanos-Dominguez, German Verleysen, Michel [UCL] Lee, John Aldo [UCL]

Accurate prediction of time series is of great interest because it can guide decisions in many economical or industrial fields. Methods can forecast either one or several steps ahead. The former is simpler and more common in many applications, while the latter is more challenging. The literature describes mainly three strategies of multi-step-ahead prediction: iterated (repeated one-step-ahead), direct, and MIMO (multiple input, multiple output). This paper proposes a MIMO strategy based on kernel adaptive filtering, which we named MSAKAF. The proposed MSAKAF has shown to be a more effective method in short, medium and long-term forecast. The proposed approach is validated on two real-world datasets. The results show that our proposal outperforms the compared baseline methods in terms of prediction accuracy.