Annual dilated convolution neural network for newbuilding ship prices forecasting

Abstract

Anticipating newbuilding ship prices is crucial for participants in the dynamic shipping market. Although the researchers from forecasting and shipping have shown that the machine learning models outperform statistical ones, convolution neural networks are not investigated. The convolution neural networks are proposed for image processing, rendering difficulty when handling monthly time series. This paper presents a light neural network with annual dilated convolution filters while extracting the newbuilding market’s short-term and long-term temporal knowledge. The multivariate shipping data are fed into multiple convolutional filters with nonlinear activations. Finally, the convoluted features are fed into a linear layer which maps the features to future values. The annual dilated convolution filter owns a vision across one year and integrates all variables’ temporal information. Besides, the dilation rate renders a parsimonious structure, preventing the model from overfitting. The proposed model is compared with statistical models, Naïve forecasts, and various machine learning models on the newbuilding prices of three tanker markets. The empirical results highlight the superiority of the proposed convolutional neural networks.