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

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.