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.

Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on EMD, grey wave and ARMA

“…In the past 20 years, only two formal papers (see Shi, 2000;Yin and Shi, 2018) were published on reputable research journals. Most of the researches focussed on the dry bulk and tanker freight rate, for example Adland and Cullinane (2006) analysed the non-linear dynamics of spot freight rates in tanker markets; Leonov and Nikolov (2012) used a hybrid model of wavelets and neural networks to study fluctuations in the freight rates of the Baltic Panamax route 2A and the Baltic Panamax route 3; Duru (2010) developed an improved fuzzy time series method to forecast Baltic Dry Index; Goulielmos and Psifia (2009) applied nonlinear methods to predict the one-year time charter weekly freight rates earned by a 65,000 dwt bulk carrier; Fan et al (2013) forecasted Baltic Exchange Dirty Tanker Index using Wavelet and Neural Networks; Shi et al (2013) used a structural vector autoregressive (SVAR) to investigate the relationship between the fluctuations in oil prices and the Baltic Dirty Tanker Index. Not only does this paper forecast CCFI with a hybrid method but also this paper analyses CCFI to confirm its seasonal patterns and cyclicity.…”

Econometric analyses of liquefied petroleum gas and product tanker shipping markets

“…A newly emerged area is artificial intelligence techniques based on statistical learning theory (Li and Parsons, 1997;Lyridis et al, 2004;Fan et al, 2013;Han et al, 2014). Such methods have a good fitting ability for complex nonlinear function (Han et al, 2014).…”