This paper is about applying recurrent least squares support vector machines (LS-SVM) on three ESTSP08 competition datasets. Least squares

support vector machines are used as nonlinear models in order to avoid local

minima problems. Then prediction task is re-formulated as function approximation

task. Recurrent LS-SVM uses nonlinear autoregressive exogenous (NARX) model

to build nonlinear regressor, by estimating in each iteration the next output value,

given the past output and input measurements.