Abstract
The recent and promising machine learning technique called support vector machine (SVM) has become a hot research subject in time series forecasting, since proposed from Statistic Learning Theory by Vapnik. As an important application of time series forecasting, reliability prediction by analyzing the historical time series data of system condition to predict the future system behaviour and/or diagnose the possible system fault, has been solved successfully by SVM with high forecasting accuracy. For this, the critical problem is the selection of SVM parameters. Many methods have been proposed, such as genetic algorithm, particle swarm optimization and analytic selection; but there is no generally structured way, yet. In this paper, the capability of SVM to perform function fitting and reliability forecasting based on different methods is investigated by experimenting on both artificial and real-world data. A comparison of the methods is offered on criteria of prediction accuracy and robustness. Finally, an attempt is made to obtain a comparative optimal parameter selection method.
