Improved multiple kernel extreme learning machine based on AdaBoost.RT

In this paper, an improved multiple kernel extreme learning machine is proposed for multivariate time series prediction. The time series is first phase-space reconstructed to form the input and output samples and then an ensemble of multiple kernel extreme learning machine is proposed based on AdaBoost.RT to achieve an improved model. In the process of model training, the weights of the training samples are adjusted according to their training error and the training samples with greater error would obtain heavier weights and be focused on to be learned. The final proposed model is a weighted ensemble of the multiple kernel extreme learning machine. The experimental results of Lorenz chaotic multivariate dynamic system and the annual runoff and sunspot multivariate dynamic system demonstrate that the proposed model has better prediction performance.