Abstract
Container freight rate forecasts are used by major stakeholders in the maritime industry, such as shipping lines, consumers, shippers, and others, to make operational decisions. Because container shipping lacks a structured forwards market, it must rely on forecasts for hedging reasons. This research is dedicated to investigating and predicting shipping containerised freight rates using machine learning approaches and real-time data to uncover superior forecasting methods. Ensemble models including Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and deep learning, in particular Multi-Layer Perceptions (MLP) have all been used to provide data-driven predictions after initial feature engineering. These three regression-based machine learning (ML) models are used to predict the container shipping rates in the North American TransBorder Freight dataset from 2006 to 2021. It has been found that MLP surpasses ensemble models with a test accuracy rate of 97%. Although our findings are drawn from American shipping data, the proposed approach serves as a general method for other international markets.
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Coraddu, A., Oneto, L., Baldi, F., Cipollini, F., Atlar, M., Savio, S.: Data-driven ship digital twin for estimating the speed loss caused by the marine fouling. Ocean Eng. 186, 106063 (2019)
China Transforms the Trucking Business - Bloomberg. https://www.bloomberg.com/opinion/articles/2017-11-30/china-transforms-the-trucking-business
The Voice of America’s Trucking Industry. https://www.trucking.org/
Clarksons Research: Container Intelligence Quarterly (January 2022). https://www.crsl.com/acatalog/container-intelligence-quarterly.html
Jeon, J.-W., Duru, O., Munim, Z.H., Saeed, N.: System dynamics in the predictive analytics of container freight rates. Transp. Sci. 55, 815–967 (2021)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Wang, Y., Meng, Q.: Optimizing freight rate of spot market containers with uncertainties in shipping demand and available ship capacity. Transp. Res. Part B Methodol. 146, 314–332 (2021)
Yan, R., Wang, S., Zhen, L., Laporte, G.: Emerging approaches applied to maritime transport research: past and future. Commun. Transp. Res. 1, 100011 (2021)
Ubaid, A., Hussain, F.K., Charles, J.: Machine learning-based regression models for price prediction in the Australian container shipping industry: case study of Asia-Oceania trade lane. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) AINA 2020. AISC, vol. 1151, pp. 52–59. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44041-1_5
Munim, Z.H., Schramm, H.-J.: Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models. Marit. Econ. Logist. 23, 310–327 (2020)
Ubaid, A., Hussain, F., Charles, J.: Modeling shipment spot pricing in the Australian container shipping industry: case of Asia-Oceania trade lane. Knowl. Based Syst. 210, 106483 (2020)
Wang, Y., Meng, Q.: Integrated method for forecasting container slot booking in intercontinental liner shipping service. Flex. Serv. Manuf. J. 31(3), 653–674 (2019)
Viellechner, A., Spinler, S.: Novel data analytics meets conventional container shipping: predicting delays by comparing various machine learning algorithms. In: Proceedings of the 53rd Hawaii International Conference on System Sciences (2020)
Le, L.T., Lee, G., Park, K.-S., Kim, H.: Neural network-based fuel consumption estimation for container ships in Korea. Marit. Policy Manage. 47(5), 615–632 (2020)
Barua, L., Zou, B., Noruzoliaee, M., Derrible, S.: A gradient boosting approach to understanding airport runway and taxiway pavement deterioration. Int. J. Pavement Eng. 22(13), 1673–1687 (2021)
TransBorder freight data \(|\) bureau of transportation statistics. https://www.bts.gov/transborder
Alexandropoulos, S.-A.N., Kotsiantis, S.B., Vrahatis, M.N.: Data preprocessing in predictive data mining. Knowl. Eng. Rev. 34, E1 (2019)
Tsaganos, G., Nikitakos, N., Dalaklis, D., Ölcer, A., Papachristos, D.: Machine learning algorithms in shipping: improving engine fault detection and diagnosis via ensemble methods. WMU J. Marit. Affairs 19, 51–72 (2020). https://doi.org/10.1007/s13437-019-00192-w
Berry, M.W., Mohamed, A., Yap, B.W. (eds.): Supervised and Unsupervised Learning for Data Science. USL, Springer, Cham (2020). https://doi.org/10.1007/978-3-030-22475-2
Liu, Y., Browne, W.N., Xue, B.: Adapting bagging and boosting to learning classifier systems. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 405–420. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77538-8_28
Zhou, Z.-H.: Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC (2019)
Zhang, W., Wu, C., Zhong, H., Li, Y., Wang, L.: Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci. Front. 12(1), 469–477 (2021)
Islam, S., Sholahuddin, A., Abdullah, A.: Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah. J. Phys. Conf. Ser. 1722(1), 012016 (2021)
Xu, A., Chang, H., Xu, Y., Li, R., Li, X., Zhao, Y.: Applying artificial neural networks (ANNs) to solve solid waste-related issues: a critical review. Waste Manage. 124, 385–402 (2021)
Yang, L., Shami, A.: On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415, 295–316 (2020)
Chicco, D., Warrens, M.J., Jurman, G.: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci. 7, e623 (2021)
3.9.6 documentation. https://docs.python.org/3/
scikit-learn: machine learning in Python—scikit-learn 0.24.2 documentation. https://scikit-learn.org/stable/
PyTorch. https://www.pytorch.org
Cloud computing services. https://cloud.google.com/
Sun, H., Lam, J.S.L., Zeng, Q.: The dual-channel sales strategy of liner slots considering shipping e-commerce platforms. Comput. Ind. Eng. 159, 107516 (2021)
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Khan, I.A., Hussain, F.K. (2022). Regression Analysis Using Machine Learning Approaches for Predicting Container Shipping Rates. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_23
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