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Maritime Policy & Management
The flagship journal of international shipping and port research
Volume 46, 2019 - Issue 2
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Articles

Forecasting container throughput based on wavelet transforms within a decomposition-ensemble methodology: a case study of China

Gang XieCFS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, ChinaCorrespondence[email protected]
ORCID Iconhttp://orcid.org/0000-0003-1421-4838View further author information
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Yatong QianCFS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, ChinaORCID Iconhttp://orcid.org/0000-0001-5794-8896View further author information
ORCID Icon &
Hewei YangCFS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, ChinaORCID Iconhttp://orcid.org/0000-0001-6265-3611View further author information
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Pages 178-200 | Published online: 23 May 2018

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Minato Nakashima & Ryuichi Shibasaki. (2023) Can AIS data improve the short-term forecast of weekly dry bulk cargo port throughput? - a machine-learning approach. Maritime Policy & Management 0:0, pages 1-17.
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Articles from other publishers (15)

Anurag Kulshrestha, Abhishek Yadav, Himanshu Sharma & Shikha Suman. (2024) A deep learning‐based multivariate decomposition and ensemble framework for container throughput forecasting. Journal of Forecasting.
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Di Zhang, Xinyuan Li, Chengpeng Wan & Jie Man. (2024) A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports. Maritime Economics & Logistics 26:1, pages 44-73.
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Yi Xiao, Minghu Xie, Yi Hu & Ming Yi. (2023) Effective multi‐step ahead container throughput forecasting under the complex context. Journal of Forecasting 42:7, pages 1823-1843.
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Shuang Yuan, Peng Jia & Shouyang Wang. (2023) A secondary decomposition–ensemble approach to interval predicting China’s railway container volume. Applied Soft Computing 143, pages 110374.
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Mustapha Oudani, Anass Sebbar, Karim Zkik & Amine Belhadi. (2023) A Prescriptive Analytics Approach for Port Logistics Planning. A Prescriptive Analytics Approach for Port Logistics Planning.
Yi Xiao, Xiaofei Xue, Yi Hu & Ming Yi. (2023) Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia. Transportation Research Record: Journal of the Transportation Research Board 2677:6, pages 530-547.
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Ziaul Haque Munim, Cemile Solak Fiskin, Bikram Nepal & Mohammed Mojahid Hossain Chowdhury. (2023) Forecasting container throughput of major Asian ports using the Prophet and hybrid time series models. The Asian Journal of Shipping and Logistics 39:2, pages 67-77.
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Jiahuan Jin, Mingyu Ma, Huan Jin, Tianxiang Cui & Ruibin Bai. (2023) Container terminal daily gate in and gate out forecasting using machine learning methods. Transport Policy 132, pages 163-174.
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Dong Huang, Manel Grifoll, Jose A. Sanchez-Espigares, Pengjun Zheng & Hongxiang Feng. (2022) Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic. Transport Policy 128, pages 1-12.
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Ying Lin, Megawati Soekarno & Yangbo Wu. 2022. Multimedia Technology and Enhanced Learning. Multimedia Technology and Enhanced Learning 234 246 .
Hao Qin. (2021) Trajectory Tracking Method of Volleyball Player’s Arm Hitting Image Based on D-P Algorithm. Scientific Programming 2021, pages 1-9.
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Sonali Shankar, Sushil Punia & P. Vigneswara Ilavarasan. (2021) Deep learning-based container throughput forecasting: a triple bottom line approach. Industrial Management & Data Systems 121:10, pages 2100-2117.
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Linli Xue, Yushan Zhu, Tao Guan, Bingyu Ren, Dawei Tong & Binping Wu. (2020) Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm. Applied Sciences 10:20, pages 7273.
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Gang Xie, Jian Zhang, Boyu Yang & Shouyang Wang. 2020. Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery 452 459 .
Sonali Shankar, P. Vigneswara Ilavarasan, Sushil Punia & Surya Prakash Singh. (2019) Forecasting container throughput with long short-term memory networks. Industrial Management & Data Systems 120:3, pages 425-441.
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