Abstract
Several combined forecasting techniques are investigated in this paper. Six baseline individual predictors are selected as basic combination components. Experimental results demonstrate that the combined predictors can significantly reduce error rates and provide a large improvement in stability and robustness. It reveals that the techniques are practically promising in the traffic domain.
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Freeway Performance Measurement System (PeMS), http://pems.eecs.berkeley.edu
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Zhang, Y., Liu, Y. (2009). Application of Combined Forecasting Models to Intelligent Transportation Systems. In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_28
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DOI: https://doi.org/10.1007/978-3-540-92814-0_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92813-3
Online ISBN: 978-3-540-92814-0
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