Recurrent sparse support vector regression machines trained by active learning in the time-domain

A method for the sparse solution of recurrent support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity and allows the user to adjust the complexity of the resulting model. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on the accuracy of the fully recurrent model using the active learning principle applied to the successive time-domain data. The user can adjust the training time by selecting how often the hyper-parameters of the algorithm should be optimised. The advantages of the proposed method are illustrated on several examples, and the experiments clearly show that it is possible to reduce the number of support vectors and to significantly improve the accuracy versus complexity of recurrent support vector regression machines.

► We propose time domain active learning for the sparse solution of recurrent SVR. ► Active learning principle is extended to the time domain modelling. ► The proposed algorithm achieves high accuracy while keeping the complexity low. ► The complexity is reduced by keeping the number of support vectors low. ► Several algorithm variants are proposed with different model training time and accuracy.