The Analysis for the Cargo Volume with Hybrid Discrete Wavelet Modeling
Yi Xiao (),
Shouyang Wang (),
Ming Xiao (),
Jin Xiao () and
Yi Hu
Additional contact information
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
Abstract:
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)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622015500285
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:16:y:2017:i:03:n:s0219622015500285
Ordering information: This journal article can be ordered from
DOI: 10.1142/S0219622015500285
Access Statistics for this article
International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi
More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().