Abstract
Conventional hybrid forecasting model has been widely used in various forecasting problems, but the sum of weights is limited to 2. An improved hybrid model named as relaxed hybrid model is proposed in this study, where the weights are relaxed to positive real data. The weights are searched by particle swarm optimization algorithm with compress factor technique. The relaxed hybrid model is employed to railway passenger turnover forecasting. The forecasting results show that our proposed model is an effective model for nonlinear time series forecasting.
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Acknowledgments
This work is financially supported by the Natural Science Research Project of Education Department of Henan Province (2011A110018). The authors would like to thank the reviewers’ suggestions.
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© 2012 Springer-Verlag London Limited
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Chen, X., Zhu, S. (2012). Relaxed Hybrid Forecasting and its Application to Railway Passenger Turnover. In: Wang, X., Wang, F., Zhong, S. (eds) Electrical, Information Engineering and Mechatronics 2011. Lecture Notes in Electrical Engineering, vol 138. Springer, London. https://doi.org/10.1007/978-1-4471-2467-2_137
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DOI: https://doi.org/10.1007/978-1-4471-2467-2_137
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