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.
Highlights
Research on tanker shipping generally focus on freight rates, fleet arrangements, ship chartering decisions, shipping strategies, operation optimization, etc. [1,2,3,4,5,6], the freight rate complexity in connection to forecasting techniques is of particular interest in this paper
This paper offers new thoughts on the application of hybrid artificial intelligent techniques—Wavelet Neural Networks, beyond traditional modeling and forecasting methods in the shipping sector
The results show that the ARIMA model has relatively low predictive accuracy in forecasting Baltic Exchange Dirty Tanker Index (BDTI), especially in longer terms
Summary
Research on tanker shipping generally focus on freight rates, fleet arrangements, ship chartering decisions, shipping strategies, operation optimization, etc. [1,2,3,4,5,6], the freight rate complexity in connection to forecasting techniques is of particular interest in this paper. [1,2,3,4,5,6], the freight rate complexity in connection to forecasting techniques is of particular interest in this paper. Decision makers in the shipping industry often utilize information on historical freight rates to make strategic decisions [cf.8], appropriate forecasting techniques may enable the actors in shipping business to make better decisions. The crude oil tanker freight rate freight forward agreements (FFA) are mainly referred to the routes included in the Worldscale [9] and Baltic Dirty Tanker Index [10]. Due to the short history of the BDTI research concerning forecasting of crude oil tanker indexes and rates has only emerged in the last decade. This paper offers new thoughts on the application of hybrid artificial intelligent techniques—Wavelet Neural Networks, beyond traditional modeling and forecasting methods in the shipping sector
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