Comparison of Different Approaches of Machine Learning Methods with Conventional Approaches on Container Throughput Forecasting
Source abstract
Abstract
Container transportation is an important mode of international trade logistics in the world today, and its changes will seriously affect the development of the international market. For example, the COVID-19 pandemic has added a huge drag to global container logistics. Therefore, the accurate forecasting of container throughput can make a significant contribution to stakeholders who want to develop more accurate operational strategies and reduce costs. However, the current research on port container throughput forecasting mainly focuses on proposing more innovative forecasting methods on a single time series, but lacks the comparison of the performance of different basic models in the same time series and different time series. This study uses nine methods to forecast the historical throughput of the world’s top 20 container ports and compares the results within and between methods. The main findings of this study are as follows. First, GRU is a method that can produce more accurate results (0.54–2.27 MAPE and 7.62–112.48 RMSE) with higher probability (85% for MAPE and 75% for RMSE) when constructing container throughput forecasting models. Secondly, NM can be used for rapid and simple container throughput estimation when computing equipment and services are not available. Thirdly, the average accuracy of machine learning forecasting methods is higher than that of traditional methods, but the accuracy of individual machine learning forecasting methods may not be higher than that of the best conventional traditional methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Maritime Ports and Logistics · Forecasting Techniques and Applications · Maritime Transport Emissions and Efficiency
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
Logistics and E-Commerce College, Zhejiang Wanli University, Ningbo 315104, China
References (58)
Show source-provided references
1. Global container shipping market development and Its impact on mega logistics system
2. A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain10.1016/j.compbiomed.2022.105461 ↗
3. Investigation of empty container shortage based on SWARA-ARAS methods in the COVID-19 era10.1186/s12544-022-00531-8 ↗
4. Goncalves, P. (2022, September 29). Global Cargo Shortage: How Iron Boxes Became Money Magnets. Available online: https://uk.finance.yahoo.com/news/global-cargo-shortage-how-iron-boxes-became-money-magnets-084858021.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS5oay8&guce_referrer_sig=AQAAABmEAd6Py72PQZcAyonGjjCKYn1SXd1Z6gx4QZosQIDBnniHitslAU66aq5KyB70obWEFH73FQ7TQpdktrWEHHIQzsuw9-gPJcf0Dx0RgaJwrJ4d1D-W-bTaFdcUUpeaRl3rnHwGtE0XIew4bpBXTSckn43NHo6lvSeg3Ijs-3a_.
5. Container throughput forecasting using a novel hybrid learning method with error correction strategy10.1016/j.knosys.2019.07.024 ↗
6. Yang, C.H., and Chang, P.Y. (2020). Forecasting the demand for container throughput using a mixed-precision neural architecture based on CNN–LSTM. Mathematics, 8.10.3390/math8101784 ↗
7. A machine learning-based forecasting system of perishable cargo flow in maritime transport10.1016/j.neucom.2019.10.121 ↗
8. Justo-Silva, R., Ferreira, A., and Flintsch, G. (2021). Review on machine learning techniques for developing pavement performance prediction models. Sustainability, 13.10.3390/su13095248 ↗
9. Armstrong, J.S. (2001). Econometric forecasting: Strategies and techniques. Principles of Forecasting: A Handbook for Researchers and Practitioners, Kluwer Academic Publishers.
10. Another look at measures of forecast accuracy10.1016/j.ijforecast.2006.03.001 ↗
11. Second-order moving average and scaling of stochastic time series10.1140/epjb/e20020150 ↗
12. Optimal length of moving average to forecast futures basis
13. Selection of the order of an autoregressive model by Akaike’s information criterion10.1093/biomet/63.1.117 ↗
14. Autoregressive model fitting for control10.1007/BF02479221 ↗
15. A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances10.1023/A:1007707430416 ↗
16. On a mixture autoregressive model10.1111/1467-9868.00222 ↗
17. Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Sons.
18. Time series forecasting using a hybrid ARIMA and neural network model10.1016/S0925-2312(01)00702-0 ↗
19. ARIMA models to predict next-day electricity prices10.1109/TPWRS.2002.804943 ↗
20. Forecasting of COVID19 per regions using ARIMA models and polynomial functions10.1016/j.asoc.2020.106610 ↗
21. Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach10.1016/j.tre.2018.12.005 ↗
22. Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences10.1016/S1352-2310(97)00447-0 ↗
23. Botalb, A., Moinuddin, M., Al-Saggaf, U., and Ali, S.S. (2018, January 13–14). Contrasting convolutional neural network (CNN) with multi-layer perceptron (MLP) for big data analysis. Proceedings of the 2018 International conference on intelligent and advanced system (ICIAS), Kuala Lumpur, Malaysia.10.1109/ICIAS.2018.8540626 ↗
24. Li, D., Huang, F., Yan, L., Cao, Z., Chen, J., and Ye, Z. (2019). Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron: Comparisons with multilayer-perceptron-only, bp neural network, and information value models. Appl. Sci., 9.10.3390/app9183664 ↗
25. Comparison multi-layer perceptron and linear regression for time series prediction of novel coronavirus covid-19 data in West Java10.1088/1742-6596/1722/1/012021 ↗
26. Moon, J., Kim, Y., Son, M., and Hwang, E. (2018). Hybrid short-term load forecasting scheme using random forest and multilayer perceptron. Energies, 11.10.3390/en11123283 ↗
27. Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm10.1016/j.resourpol.2019.02.014 ↗
28. Short-term rainfall forecasting using multi-layer perceptron10.1109/TBDATA.2018.2871151 ↗
29. Multilayer perceptron for short-term load forecasting: From global to local approach10.1007/s00521-019-04130-y ↗
30. Kelleher, J.D. (2019). Deep Learning, MIT Press.10.7551/mitpress/11171.001.0001 ↗
31. Machine learning and deep learning10.1007/s12525-021-00475-2 ↗
32. Deep learning10.1038/nature14539 ↗
33. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network10.1016/j.physd.2019.132306 ↗
34. Noman, A.A., Heuermann, A., Wiesner, S.A., and Thoben, K.D. (2021, January 19–22). Towards Data-Driven GRU based ETA Prediction Approach for Vessels on both Inland Natural and Artificial Waterways. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.10.1109/ITSC48978.2021.9564883 ↗
35. Chen, X., and Huang, L. (2020, January 11–13). Port Throughput Forecast Model Based on Adam Optimized GRU Neural Network. Proceedings of the 2020 4th International Conference on Computer Science and Artificial Intelligence, Zhuhai, China.10.1145/3445815.3445823 ↗
36. Forecasting container throughput with long short-term memory networks10.1108/IMDS-07-2019-0370 ↗
37. Probabilistic forecasting with temporal convolutional neural network10.1016/j.neucom.2020.03.011 ↗
38. Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., and Iosifidis, A. (2017, January 24–27). Forecasting stock prices from the limit order book using convolutional neural networks. Proceedings of the 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, Greece.10.1109/CBI.2017.23 ↗
39. Tian, C., Ma, J., Zhang, C., and Zhan, P. (2018). A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies, 11.10.3390/en11123493 ↗
40. Koprinska, I., Wu, D., and Wang, Z. (2018, January 8–13). Convolutional neural networks for energy time series forecasting. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.10.1109/IJCNN.2018.8489399 ↗
41. Nonpooling convolutional neural network forecasting for seasonal time series with trends10.1109/TNNLS.2019.2934110 ↗
42. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017, January 4–9). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA.
43. GPT-3: Its nature, scope, limits, and consequences10.1007/s11023-020-09548-1 ↗
44. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv.
45. Exploring the limits of transfer learning with a unified text-to-text transformer
46. Wu, N., Green, B., Ben, X., and O’Banion, S. (2020). Deep transformer models for time series forecasting: The influenza prevalence case. arXiv.
47. Li, S., Jin, X., Xuan, Y., Zhou, X., Chen, W., Wang, Y.X., and Yan, X. (2019, January 8–14). Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Proceedings of the Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, BC, Canada.
48. Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021, January 2–9). Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event.10.1609/aaai.v35i12.17325 ↗
49. A transformer self-attention model for time series forecasting
50. Klein, J.L. (1997). Statistical Visions in Time: A History of Time Series Analysis, 1662–1938, Cambridge University Press.
51. Noriega, L. (2005). Multilayer Perceptron Tutorial, School of Computing, Staffordshire University.
52. Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.10.3115/v1/W14-4012 ↗
53. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
54. Huang, S., Tang, J., Dai, J., and Wang, Y. (2019). Signal status recognition based on 1DCNN and its feature extraction mechanism analysis. Sensors, 19.10.3390/s19092018 ↗
55. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia10.1016/j.aei.2017.11.002 ↗
56. Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv.
57. Are GRU cells more specific and LSTM cells more sensitive in motive classification of text?10.3389/frai.2020.00040 ↗
58. Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods10.1016/j.dib.2021.106759 ↗