Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks
Shuangrui Fan, Tingyun Ji, Wilmsmeier Gordon, Bergqvist Rickard
DOI: 10.4236/jtts.2013.31008   PDF    HTML   XML   7,408 Downloads   13,628 Views   Citations


Baltic Exchange Dirty Tanker Index (BDTI) is an important assessment index in world dirty tanker shipping industry. Actors in the industry sector can gain numerous benefits from accurate forecasting of the BDTI. However, limitations exist in traditional stochastic and econometric explanation modeling techniques used in freight rate forecasting. At the same time research in shipping index forecasting e.g. BDTI applying artificial intelligent techniques is scarce. This analyses the possibilities to forecast the BDTI by applying Wavelet Neural Networks (WNN). Firstly, the characteristics of traditional and artificial intelligent forecasting techniques are discussed and rationales for choosing WNN are explained. Secondly, the components and features of BDTI will be explicated. After that, the authors delve the determinants and influencing factors behind fluctuations of the BDTI in order to set inputs for WNN forecasting model. The paper examines non-linearity and non-stationary features of the BDTI and elaborates WNN model building procedures. Finally, the comparison of forecasting performance between WNN and ARIMA time series models show that WNN has better forecasting accuracy than traditionally used modeling techniques.

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Fan, S. , Ji, T. , Gordon, W. and Rickard, B. (2013) Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks. Journal of Transportation Technologies, 3, 68-87. doi: 10.4236/jtts.2013.31008.

Conflicts of Interest

The authors declare no conflicts of interest.


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