Forecasting container throughput with long short-term memory networks. Issue 3 (8th January 2020)

• Record Type: Journal Article
• Authors: Shankar, Sonali; Ilavarasan, P. Vigneswara; Punia, Sushil; Singh, Surya Prakash
• Is Part Of: Industrial management & data systems. Volume 120:Issue 3(2020)
• Page Start: 425
• Page End: 441
• Publication Date: 2020-01-08
• Subjects: Logistics -- Time series -- Forecasting -- Deep learning -- LSTM -- Maritime supply chain; Industrial management -- Periodicals; Electronic data processing -- Periodicals; Business -- Periodicals; Industrial management -- Great Britain -- Periodicals; 658.05
• DOI: 10.1108/IMDS-07-2019-0370
• Languages: English
• ISSNs: 0263-5577

Abstract

Abstract : Purpose: Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach: In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter's, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings: The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value: The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.