A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks

Qingcheng Zeng (), Chenrui Qu, Adolf K.Y. Ng () and Xiaofeng Zhao
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Qingcheng Zeng: School of Transportation Management, Dalian Maritime University, 1 Linghai Road, Dalian 116026, PR China.
Chenrui Qu: School of Transportation Management, Dalian Maritime University, 1 Linghai Road, Dalian 116026, PR China.
Adolf K.Y. Ng: Department of Supply Chain Management, I.H. Asper School of Business, University of Manitoba, Winnipeg, Canada MB R3T 5V4.
Xiaofeng Zhao: School of Transportation Management, Dalian Maritime University, 1 Linghai Road, Dalian 116026, PR China.

Maritime Economics & Logistics, 2016, vol. 18, issue 2, 192-210

Abstract: In this article, a method based on empirical mode decomposition (EMD) and artificial neural networks (ANN) is developed for Baltic Dry Index (BDI) forecasting. The original BDI series is decomposed into several independent intrinsic mode functions (IMFs) using EMD first. Then the IMFs are composed into three components: short-term fluctuations, effect of extreme events and long-term trend. On the basis of results of decomposition and composition, ANN is used to model each IMF and composed component. Results show that the proposed EMD-ANN method outperforms ANN and VAR. The EMD-based method thus provides a useful technique for dry bulk market analysis and forecasting.

Date: 2016
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