Improvement of LS-SVM for time series prediction

Improving the accuracy and speed has become a main concern of time-series prediction. Aiming at these problems existing in time-series prediction, three kinds of researches and improvements are made as follows. This paper proposes a prediction method of combining Empirical Mode Decomposition (EMD) with least squares support vector machines (LS-SVM), the experimental results show that under the same conditions, the testing error of EMD combining with LS-SVM method is 0.1943 which significantly better than any single method of LS-SVM or SVM or BP neural network (BPNN), thus it is better for non-stationary time series. The immune clonal memetic algorithm (ICMA) is employed for resolving the parameter optimization problem in LS-SVM model, by combining global optimization with local optimization, the experiments show that the testing error of this method is 0.0865, which is faster than the optimization with the genetic algorithm (GA) or grid search algorithm. To raise the prediction speed, an improved LS-SVM online prediction method is proposed, which combine selective pruning algorithm with fast incremental learning, the results of experiment show that the speed of this method is improved nearly double compared with the direct inverse LS-SVM´s, and a quarter is raised than the recursive inversion LS-SVM, with higher real-time performance while ensuring the reasonable prediction accuracy.