Annual dilated convolutional LSTM network for time charter rate forecasting

Abstract

Time charter rates must be predicted accurately to assist sensible decisions in the global, highly volatile shipping market. Time charter rates are affected by multiple factors, such as second-hand ship prices, order book, Libor interest rate, etc. However, not all factors convey predictive features to anticipate the future of time charter rates. Therefore, extracting predictive features from multiple driving time series from the shipping market is crucial for forecasting purposes. Accordingly, this paper proposes a novel convolutional recurrent neural network for time charter rates forecasting under the multi-variate phenomenon. The proposed network first extracts features from the monthly time series using a novel convolutional filter, the annual dilated filter. The annual dilated convolutional filter can extract the predictive features effectively and impose a sparse input structure to prevent overfitting. Then, a recurrent neural network learns the temporal information from the convoluted features. An extensive comparison study with many baseline models, including the persistence (Naïve I), statistical models, and the state-of-art networks, is conducted on the time charter rates of six kinds of ships. The empirical results demonstrate the proposed model’s superiority in forecasting the time charter rates.