Interval forecasting of Baltic Dry Index within a secondary decomposition-ensemble methodology

Abstract

The Baltic Dry Index (BDI) is one of the leading indexes that is the most commonly used to reflect the prosperity of the shipping industry. The index’s volatility indicates the operational risks that shipping-related enterprises and service institutions may face. In order to more accurately estimate the volatility, this study proposes a secondary decomposition-ensemble model that can be used to predict interval-valued time series (ITS) of the BDI. Four main steps are involved, namely ITS construction and primary decomposition, secondary decomposition, component ITS forecasting, and ensemble. To be specific, bivariate empirical mode decomposition (BEMD) is employed for the primary decomposition, and multivariate variational mode decomposition (MVMD) is used for the secondary decomposition. Using daily BDI data, an empirical analysis is conducted to verify the proposed model. The investigation shows that, compared to other models, the proposed method has better forecasting performance and stronger robustness in ITS forecasting of the BDI. The results indicate that using the proposed model is a promising method for the volatility estimation of complex ITS data.