Time series prediction with performance guarantee

Time series prediction has many important real applications such as network resource management and quality-of-service assurance. Many different techniques have been developed to deal with time series predictions, for example, the Box–Jenkins approach and machine learning. In this study, the authors focus on the problem of time series prediction with performance guarantees and describe two machine-learning techniques, namely prediction with expert advice and conformal predictors. The authors investigate the application of these techniques to network traffic demand and propose a novel way of combining these two techniques to provide performance guarantee on predictions. The method is generic and the authors demonstrate this approach by carrying out extensive experiments on both artificially generated data and publicly available network traffic demand datasets. Empirical results show that the proposed method can increase the performance of the prediction system.

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