Annual dilated convolutional LSTM network for time charter rate forecasting

Jixian Mo, Ruobin Gao, Jiahui Liu, Liang Du, Kum Fai Yuen

Abstract: Highlights•A novel dilated convolution LSTM neural network is proposed to forecast time charter rates.•The proposed convolutional filters can extract temporal features of monthly charter rates efficiently.•The proposed model outperforms the baselines in terms of three error metrics.