Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

Abstract

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.

Cite this paper

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.

Fan, S. , Ji, T. , Gordon, W. and Rickard, B. (2013) Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks.

References

[1] D. Hawdon, “Tanker Freight Rates in the Short and Long Run,” Applied Economics, Vol. 10, No. 3, 1978, pp. 203218. doi:10.1080/758527274

[2] D. Glen, M. Owen and R. Meer, “Spot and Time Charter Rates for Tankers, 1970-77,” Journal of Transport Economics and Policy, Vol. 15, No. 1, 1981, pp. 45-58

[3] M. Beenstock and A. Vergottis, “An Econometric Model of the World Tanker Market,” Journal of Transport Economics and Policy, Vol. 23, No. 3, 1989, pp. 263-280.

[4] A. Perakis and W. Bremer, “An Operational Tanker Scheduling Optimization System: Background, Current Practice and Model Formulation,” Maritime Policy and Management, Vol. 19, No. 3, 1992, pp. 177-187.
doi:10.1080/751248659

[5] R. Adland and K. Cullinane, “The Non-Linear Dynamics of Spot Freight Rates in Tanker Markets,” Transportation Research Part E: Logistics and Transportation Review, Vol. 42, No. 3, 2006, pp. 211-224.
doi:10.1016/j.tre.2004.12.001

[6] R. Laulajainen, “Operative Strategy in Tanker (Dirty) Shipping,” Maritime Policy and Management, Vol. 35, No. 3, 2008, pp. 315-341.

[7] T. Angelidis and S. G. Skiadopoulos, “Measuring the Market Risk of Freight Rates: A Value-at-Risk Approach,” International Journal of Theoretical and Applied Finance, Vol. 11, No. 5, 2008, pp. 447-469.
doi:10.1142/S0219024908004889

[8] M. Stopford, “Maritime Economics,” 3rd Edition, Routledge, New York, 2009. doi:10.4324/9780203891742

[9] Worldscale Association, “Introduction to Worldscale Freight Rate Schedules,” 2011.
http://www.worldscale.co.uk/company%5Ccompany.htm

[10] R. Batchelor, A. Alizadeh and I. Visvikis, “Forecasting Spot and Forward Prices in the International Freight Market,” International Journal of Forecasting, Vol. 23, No. 1, 2007, pp. 107-114.

[11] K. Cullinane, “A Short-Term Adaptive Forecasting Model for BIFFEX Speculation: A Box—Jenkins Approach,” Maritime Policy and Management: The Flagship Journal of International Shipping and Port Research, Vol. 19, No. 2, 1992, pp. 91-114.

[12] M. Kavussanos, “Price Risk Modelling of Different Sized Vessels in Tanker Industry Using Autoregressive Conditional Heteroscedasticity GARCH Models,” Transportation Research Part E: Logistics and Transportation Review, Vol. 32, No. 2, 1996, pp. 161-176.

[13] F. Jonnala, S. Fuller and D. Bessler, “A GARCH Approach to Modelling Ocean Grain Freight Rates,” International Journal of Maritime Economics, Vol. 4, No. 2, 2002, pp. 103-125. doi:10.1057/palgrave.ijme.9100039

[14] A. W. Veenstra and P. H. Franses, “A Co-Integration Approach to Forecasting Freight Rates in the Dry Bulk Shipping Sector,” Transportation Research Part A: Policy & Practice, Vol. 31, No. 6, 1997, pp. 447-458.

[15] J. Tvedt, “Shipping Market Models and the Specification of Freight Rate Processes,” Maritime Economics and Logistics, Vol. 5, No. 4, 2003, pp. 327-346.
doi:10.1057/palgrave.mel.9100085

[16] A. M. Goulielmos and M. Psifia, “A Study of Trip and Time Charter Freight Rate Indices: 1968-2003,” Maritime Policy and Management, Vol. 34, No. 1, 2007, pp. 55-67.
doi:10.1080/03088830601103418

[17] S. S?dal, S. Koekebakkera and R. Adland, “Market Switching in Shipping—A Real Option Model Applied to the Valuation of Combination Carriers,” Review of Financial Economics, Vol. 17, No. 3, 2008, pp. 183-203.
doi:10.1016/j.rfe.2007.04.001

[18] T. Koopmans, “Tanker Freight Rates and Tankship Building,” The Economic Journal, Vol. 49, No. 196, 1939, pp. 760-762. doi:10.2307/2225041

[19] Z. S. Zannetos, “The Theory of Oil Tankship Rates: An Economic Analysis of Tankship Operations,” MIT— Massachusetts Institute of Technology, Cambridge, 1964, pp. 60-64.

[20] J. J. Evans, “An Analysis of Efficiency of the Bulk Shipping Markets,” Maritime Policy and Management: The Flagship Journal of International Shipping and Port Research, Vol. 21, No. 4, 1994, pp. 311-329.

[21] S. Reutlinger, “Analysis of a Dynamic Model, with Particular Emphasis on Long-Run Projections,” Journal of Farm Economics, Vol. 48, No. 1, 1966, pp. 88-106.
doi:10.2307/1236181

[22] R. Adland and S. P. Strandenes, “A Discrete-Time Stochastic Partial Equilibrium Model of the Spot Freight Market,” Journal of Transport Economics and Policy (JTEP), Vol. 41, No. 2, 2007, pp. 189-218.

[23] Q. Zhang and A. Benveniste, “Wavelet Networks,” IEEE Transactions on Neural Networks, Vol. 3, No. 6, 1992, pp. 889-898. doi:10.1109/72.165591

[24] Z. Wang and Y. Tan, “Research of Wavelet Neural Network Based Host Intrusion Detection Systems,” Proceedings of the International Computer Conference 2006 on Wavelet Active, Chongqing, 29-31 August 2006, pp 1007-1012.

[25] K. K. Minu, M. C. Lineesh and C. J. John, “Wavelet Neural Networks for Nonlinear Time Series Analysis,” Applied Mathematical Sciences, Vol. 4, No. 50, 2010, pp. 2485-2495.

[26] K. G. Goulias, “Transport Science and Technology,” Elsevier Ltd., Amsterdam, 2007.

[27] The Baltic Exchange, “Manual for Panelists—A Guide to Freight Reporting and Index Production,” Unpublished Manuscript, The Baltic Exchange, London, 2011.

[28] D. B. Percival and A. T. Walden “Wavelet Methods for Time Series Analysis,” Cambridge University Press, Cambridge, 2006.

[29] A. Graps, “An Introduction to Wavelets,” IEEE Computational Sciences and Engineering, Vol. 2, No. 2, 1995, pp. 50-61. doi:10.1109/99.388960

[30] K. P. Soman, K. I. Ramachandran and N. G. Resmi, “Insight into Wavelets,” 3rd Edition, PHI Learning Pvt. Ltd., Coimbatore, 2010.

[31] L. Debnath, “Wavelet Transforms and Their Applications,” Springer, Boston, 2002.
doi:10.1007/978-1-4612-0097-0

[32] J. Lewalle, “Wavelets without Lemmas on Applications of Continuous Waveletsto Data Analysis,” Syracuse University, Syracuse, 1998.
http://www.ecs.syr.edu/faculty/lewalle/papers/vki1.pdf

[33] D. Veitch, “Wavelet Neural Networks and Their Application in the Study of Dynamical Systems,” Networks, Vol. 1, No. 8, 2005, pp. 313-320.

[34] I. Daubechies, “The Wavelet Transform, Time-frequency Localization and Signal Analysis,” IEEE Transactions on Information Theory Society, Vol. 36, No. 5, 1990, pp. 961-1005. doi:10.1109/18.57199

[35] G. Dreyfus, “Neural Networks: Methodology and Applications,” Springer-Verlag Berlin Heidelberg, New York, 2005.

[36] A. Abraham, “Artificial Neural Networks. Handbook of Measuring System Design,” John Wiley and Sons Ltd., Hoboken, 2005.

[37] D. P. Mandic and J. A. Chambers, “Recurrent Neural Networks for Prediction: Learning, Algorithms, Architectures and Stability,” John Wiley and Sons Ltd., Hoboken, 2001. doi:10.1002/047084535X

[38] M. Casey, “The Dynamics of Discrete-Time Computation, with Application to Recurrent Neural Networks and Finite State Machine Extraction,” Neural Computation, Vol. 8, No. 6, 1996, pp. 1135-1178.
doi:10.1162/neco.1996.8.6.1135

[39] G. Dematos, M. S. Boyd, B. Kermanshahi, N. Kohzadi and I. Kaastra, “Feedforward versus Recurrent Neural Networks for Forecasting Monthly Japanese Yen Exchange Rates,” Asia-Pacific Financial Markets, Vol. 3, No. 1, 1996, pp. 59-75.

[40] B. A. Pearlmutter, “Dynamic Recurrent Neural Networks,” School of Computer Science Carnegie Mellon University, Defense Advanced Research Projects Agency, Information Science and Technology Office, 1990.
http://www.bcl.hamilton.ie/~barak/papers/CMU-CS-90-196.pdf

[41] K. Cannons and V. Cheung, “An Introduction to Neural Networks,” Iowa State University, Ames, 2002.
http://www2.econ.iastate.edu/tesfatsi/NeuralNetworks.CheungCannonNotes.pdf

[42] R. Hecht-Nielsen, “Theory of the Backpropagation Neural Network,” International Joint Conference on Neural Networks (IJCNN), Vol. 1, Washington DC, 18-22 June 1989, pp. 593-605.

[43] P. J. Werbos, “Backpropagation Through Time: What It Does and How To Do It,” Proceedings of the IEEE, Vol. 78, No. 10, 1990, pp. 1550-1560. doi:10.1109/5.58337

[44] J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, San Francisco, 2006.

[45] T. Hastie, R. Tibshirani and J. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Springer, New York, 2009.

[46] P. K. Simpson, “Neural Networks Theory, Technology, and Applications, Institute of Electrical and Electronics Engineers, Technical Activities Board,” University of Michigan, Ann Arbor, 1996.

[47] R. M. Golden, “Mathematical Methods for Neural Network Analysis and Design,” The MIT Press, Cambridge, 1996.

[48] K. M. Vu, “The ARIMA and VARIMA Time Series: Their Modelings, Analyses and Applications,” AuLac Technologies Inc., Ottawa, 2007.

[49] T. C. Mills, “Time Series Techniques for Economists,” Cambridge University Press, Cambridge, 1990.

[50] Yahoo, “Get Quotes, Historical Prices,” 2012.
http://finance.yahoo.com/

[51] B. D. Ripley, “Pattern Recognition and Neural Networks,” Cambridge University Press, Cambridge, 1996.

[52] The 20 trading days is based on the following assumption: 1 week = 5 trading days, 1 month = 4 week.

[53] A. H. El-Shaarawi and W. W. Piegorsch, “Encyclopedia of Environmetrics,” Wiley, Hoboken, 2002.

[54] A. Aussem and F. Murtagh, “Combining Neural Network Forecasts on Wavelet-Transformed Time Series,” Connection Science, Vol. 9, No. 1, 1997, pp. 113-122.
doi:10.1080/095400997116766

[55] J. J. Merelo, M. Patón, A. Ca?as, A. Prieto and F. Morán, “Optimization of a Competitive Learning Neural Network by Genetic Algorithms,” New Trends in Neural Computation, Vol. 686, 1993, pp. 185-192.
doi:10.1007/3-540-56798-4_145

[56] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Vol. 4, Perth, 27 November-1 December 1995, pp. 1942-1948.
doi:10.1109/ICNN.1995.488968