An Interval Knowledge Based Forecasting Paradigm for Container Throughput Prediction

Huang, Anqiang; Lai, Kin Keung; Qiao, Han; Wang, Shouyang; Zhang, Zhenji

doi:10.1016/j.procs.2015.07.126

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Container throughputs require that ports play an important role in domestic goods exchange and foreign trade transportation, which are important indicators of global trade ( Loske, 2020 ). Therefore, port container throughputs, as an important indicator of container development, can intuitively reflect the development trends of ports and provide an essential basis for studying the development of port container transportation ( Huang et al, 2015 ). Four major ports, namely Shanghai Port, the Port of Hong Kong, the Port of Singapore and the Port of Los Angeles, were chosen to demonstrate the development of ports in this study.…”

Section: Methodsmentioning

confidence: 99%

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19

Zhao

et al. 2022

Transport Policy

View full text Add to dashboard Cite

Following the outbreak of the COVID-19 pandemic, various lockdown strategies restrained global economic growth bringing a significant decline in maritime transportation. However, the previous studies have not adequately recognized the specific impacts of COVID-19 on maritime transportation. In this study, a series of analyses of the Baltic Dry Index (BDI), the China Coastal Bulk Freight Index (CCBFI) and of container throughputs with and without the impact of COVID-19 were carried out to assess changing trends in dry bulk and container transportation. The results show that global dry bulk transportation was largely affected by lockdown policies in the second month during COVID-19, and BDI presented a year-on-year decrease of approximately 35.5% from 2019 to 2020. The CCBFI showed an upward trend in the second month during COVID-19, one month ahead of the BDI. The container throughputs at Shanghai Port, the Ports of Hong Kong, the Ports of Singapore and the Ports of Los Angeles from 2019 to 2020 presented the largest year-on-year drops of approximately 19.6%, 7.1%, 10.6% and 30.9%, respectively. In addition, the authors developed exponential smoothing models of BDI, CCBFI, and container transportation, and calculated the percentage prediction error between the observed and predicted values to examine the impact of exogenous effects on the shipping industry due to the outbreak of COVID-19. The results are consistent with the conclusions obtained from the comparison of BDI, CCBFI, and container transportation during the same period in 2020 and 2019. Finally, on the basis of the findings, smart shipping and special support policies are proposed to reduce the negative impacts of COVID-19.

show abstract

Section: Methodsmentioning

confidence: 99%

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19

Zhao

et al. 2022

Transport Policy

View full text Add to dashboard Cite

show abstract

“…It plays an important role in domestic cargo exchange and foreign trade transportation and serves as a crucial measurement standard for global trade (Loske, 2020). It can directly reflect the development trends of ports (Huang et al, 2015). Therefore, as an important indicator of container development, port container throughput can provide a fundamental basis for studying the trends of the CCFI.…”

Section: Major Ports: Container Throughputmentioning

confidence: 99%

Analysis of influencing factors and prediction of China’s Containerized Freight Index

Tu,

Yang,

Lin

et al. 2023

Front. Mar. Sci.

View full text Add to dashboard Cite

China, as a major maritime nation, the China Containerized Freight Index (CCFI) serves as an objective reflection of the Chinese shipping market and an important indicator for understanding China’s shipping industry globally. The shipping market is a complex ecosystem influenced by various factors, including vessel supply and demand, cargo supply and demand relationships and prices, fuel prices, and competition from substitute and complementary markets. To analyze and study the state of the Chinese shipping market, we selected the CCFI as an indicator and collected data on six factors that may affect the overall shipping market. These factors include “ the China Coastal Bulk Freight Index(CCBFI)”, “the Baltic Dry Index(BDI)”, “the Yangtze River Container Freight Index”, “Global: Aluminum (minimum purity of 99.5%, London Metal Exchange (LME) spot price): UK landed price”, “Major Ports: Container Throughput”, and “Coal Price: US Central Appalachia: Coal Spot Price Index”. Then, we constructed an analyticaland predictive framework using Deep Neural Network (DNN), CatBoost regression model, and robust regression model to study the CCFI. Based on the R2 results of the three models, it is evident that DNN provides the best analytical and predictive performance for the CCFI, accurately forecasting its changes. Additionally, the robust regression model indicates that “Global: Aluminum (minimum purity of 99.5%, LME spot price): UK landed price” has the greatest impact on the CCFI. Finally, from a business perspective, we provide some suggestions for China’s container shipping industry.

show abstract

Improving Forecasting Performance by Exploiting Expert Knowledge: Evidence from Guangzhou Port

Huang

Qiao

Wang

et al. 2016

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

Expert knowledge has been proved by substantial studies to be contributory to higher forecasting performance; meanwhile, its application is criticized and opposed by some groups for biases and inconsistency inherent in experts’ subjective judgment. This paper proposes a new approach to improving forecasting performance, which takes advantage of expert knowledge by constructing a constraint equation rather than directly adjusting the predicted values by experts. For the comparison purpose, the proposed approach, together with several widely used models including ARIMA, BP-ANN and the judgment model (JM), is applied to forecasting the container throughput of Guangzhou Port, which is one of the most important ports of China. Forecasting performances of the above models are compared and the results clearly show superiority of the proposed approach over its rivals, which implies that expert knowledge will make positive contribution as long as it is used in a right way.

show abstract

An Interval Knowledge Based Forecasting Paradigm for Container Throughput Prediction

Cited by 4 publications

References 22 publications

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19

Analysis of influencing factors and prediction of China’s Containerized Freight Index

Improving Forecasting Performance by Exploiting Expert Knowledge: Evidence from Guangzhou Port

Contact Info

Product

Resources

About