The Analysis for the Cargo Volume with Hybrid Discrete Wavelet Modeling
Yi Xiao (),
Shouyang Wang (),
Ming Xiao (),
Jin Xiao () and
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Yi Xiao: School of Information Management, Central China Normal University, Wuhan 430079, P. R. China
Shouyang Wang: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China
Ming Xiao: Network Center, Central China Normal University, Wuhan 430079, P. R. China
Jin Xiao: Business School, Sichuan University, Chengdu 610064, P. R. China
Yi Hu: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
International Journal of Information Technology & Decision Making (IJITDM), 2017, vol. 16, issue 03, 851-863
Many efforts have been made to the development of models that able to analyze and predict marine cargo volume. However, improving forecasting especially marine cargo throughput time series forecasting accuracy is an important yet often difficult issue facing managers. In this study, a TEI@I methodology based hybrid forecasting model is proposed. The original time series are decomposed different scale components using discrete wavelet technique based on seasonality analysis of components. All decomposed components are predicted by radial basis function networks due to its flexible nonlinear modeling capability. Empirical results suggest that the use of discrete wavelet technique enhances the ability of monthly volatility mining and demonstrate consistent better performance of the proposed approach.
Keywords: Cargo volume analyzing; radial basis function network; discrete wavelet technique; TEI@I methodology (search for similar items in EconPapers)
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