Forecasting the Baltic Dry Index by using an artificial neural network approach
Forecasting the Baltic Dry Index by using an artificial neural network approach
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.
___
- Pino R, Parreno J, Gomez A, Priore P. Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Eng Appl Artif Intel 2008; 21: 53-62.
- Azadeh A, Ghaderi SF, Sohrabkhani, S. Forecasting electrical consumption by integration of neural network, time series and ANOVA. Appl Math Comput 2007; 186: 1753-1761.
- Ruan Q, Wang Y, Lu X, Qin J. Cross-correlations between Baltic Dry Index and crude oil prices. Physica A 2016; 453: 278-289.
- Uyar K, İlhan Ü, İhan A. Long term dry cargo freight rates forecasting by using recurrent fuzzy neural networks.
Procedia Computer Science 2016; 102:642-647.
- Duru O, Bulut E, Yoshida S. Bivariate long term fuzzy time series forecasting of dry cargo freight rates. Asian
Journal of Shipping and Logistics 2010; 26: 205-223.
- Wong HL. BDI forecasting based on fuzzy set theory, grey system and ARIMA. In: 2014 International Conference on
Industrial, Engineering and Other Applications of Applied Intelligent Systems; 3–6 June 2014; Kaohsiung, Taiwan. pp. 140-149.
- Jianmin B, Pan L, Xie Y. A new BDI forecasting model based on support vector machine. In: IEEE 2016 Information Technology, Networking, Electronic and Automation Control Conference; 20–22 May 2016; Chongqing, China. New York, NY, USA: IEEE. pp. 65-69.
- Zeng Q, Qu C, Ng AKY, Zhao X. A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks. Marit Econ Logist 2016; 18: 192-210.
- Zeng Q, Qu C. An approach for Baltic Dry Index analysis based on empirical mode decomposition. Marit Policy Manag 2014; 41: 224-240.
- Bildirici ME, Kayık¸cı F, Onat IS¸. Baltic Dry Index as a major economic policy indicator: the relationship with economic growth. Procedia - Social and Behavioral Sciences 2015; 210: 416-424.
- Moazzami M, Hooshmand RA. Short-term nodal congestion price forecasting in a large-scale power market using ANN with genetic optimization training. Turk J Elec Eng & Comp Sci 2012; 20: 751-768.
- Kölmek M, Navruz İ. Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks. Turk J Elec Eng & Comp Sci 2015; 23: 841-852.
- Cankurt S, Suba¸sı A. Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey. Turk J Elec Eng & Comp Sci 2016; 24: 3388-3404.
- Mostafa, Mohamed M. Forecasting the Suez Canal traffic: a neural network analysis. Marit Policy Manag 2004; 31:139-156.
- Li J, Michael GP. Forecasting tanker freight rate using neural networks. Marit Policy Manag 1997; 24: 9-30.
- Singh V, Indra G, Gupta HO. ANN-based estimator for distillation using Levenberg-Marquardt approach. Eng Appl Artif Intel 2007; 20: 249-259.
- Zounemat-Kermani M. Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorol Atmos Phys 2012; 117: 181-192.
- Goulielmos AM, Psifia ME. Forecasting weekly freight rates for one-year time charter 65000 dwt bulk carrier, 1989-2008, using nonlinear methods. Marit Policy Manag 2009; 36: 411-436.
- Schinas O, Grau C, Johns M. HSBA Handbook on Ship Finance. Berlin, Germany: Springer, 2015.