Abstract
Based on the principle of Bayesian theory-based forecasting, a new forecasting model, called Bayesian Least Risk Forecasting model, is proposed in this paper. Firstly, the principle and modeling idea of Bayesian forecasting are illustrated with the explanation of the meaning of least risk forecasting. Then the advantages and learning algorithm of this model are discussed explicitly. In order to validate the prediction performance of Bayesian Least Risk Forecasting model, a simulated time series and practical data measured from some rotating machinery are used to compare the ability of prediction with classical artificial neural networks model. The results show that the bayesian model can contribute to a good accuracy of prediction.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wen, G., Zhang, X. (2006). A New Time Series Forecasting Approach Based on Bayesian Least Risk Principle. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_72
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DOI: https://doi.org/10.1007/11881070_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
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