Forecasting the Baltic Dry Index by using an artificial neural network approach

Authors

BEKİR ŞAHİN
SAMET GÜRGEN
BEDİR ÜNVER
İSMAİL ALTIN

DOI

10.3906/elk-1706-155

Abstract

The Baltic Dry Index (BDI) is a robust indicator in the shipping sector in terms of global economic activities, future world trade, transport capacity, freight rates, ship demand, ship orders, etc. It is hard to forecast the BDI because of its high volatility and complexity. This paper proposes an artificial neural network (ANN) approach for BDI forecasting. Data from January 2010 to December 2016 are used to forecast the BDI. Three different ANN models are developed: (i) the past weekly observation of the BDI, (ii) the past two weekly observations of the BDI, and (iii) the past weekly observation of the BDI with crude oil price. While the performance parameters of these three models are close to each other, the most consistent model is found to be the second one. Results show that the ANN approach is a significant method for modeling and forecasting the BDI and proving its applicability.

Keywords

Baltic Dry Index, forecasting, artificial neural network, crude oil, shipping industry

First Page

1673

Last Page

1684

Recommended Citation

ŞAHİN, BEKİR; GÜRGEN, SAMET; ÜNVER, BEDİR; and ALTIN, İSMAİL (2018) "Forecasting the Baltic Dry Index by using an artificial neural network approach," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 26: No. 3, Article 44. https://doi.org/10.3906/elk-1706-155
Available at: https://journals.tubitak.gov.tr/elektrik/vol26/iss3/44