TY - JOUR
T1 - Is entropy an indicator of port traffic predictability? The evidence from Chinese ports
AU - Li, Chuchu
AU - Lin, Qin
AU - Huang, Dong
AU - Grifoll, Manel
AU - Yang, Dong
AU - Feng, Hongxiang
N1 - Funding Information:
The authors would like to thank the anonymous referees for their helpful comments on this paper. Their comments significantly increased the quality of this paper. We sincerely thank Prof. Zhongzhen Yang from the Faculty of Maritime and Transportation at Ningbo University and Prof. Jose A. Sanchez-Espigares from the Department of Statistics and Operation Research at Universitat Politècnica de Catalunya (UPC-Barcelona Tech) for their valuable suggestions on the revision of this paper. This work was sponsored by K.C. Wong Magna Fund in Ningbo University, China and 111 Project [grant number D21013 ] and the General Public Welfare Project of Zhejiang Science and Technology Department, China [grant number 2018LGF18E090005 ].
Funding Information:
The authors would like to thank the anonymous referees for their helpful comments on this paper. Their comments significantly increased the quality of this paper. We sincerely thank Prof. Zhongzhen Yang from the Faculty of Maritime and Transportation at Ningbo University and Prof. Jose A. Sanchez-Espigares from the Department of Statistics and Operation Research at Universitat Politècnica de Catalunya (UPC-Barcelona Tech) for their valuable suggestions on the revision of this paper. This work was sponsored by K.C. Wong Magna Fund in Ningbo University, China and 111 Project [grant number D21013] and the General Public Welfare Project of Zhejiang Science and Technology Department, China [grant number 2018LGF18E090005].
Publisher Copyright:
© 2023
PY - 2023/2/15
Y1 - 2023/2/15
N2 - Historical time-series data of container traffic in ports are characterised by strong randomness that causes the time-series prediction performance to vary significantly. In this sense, the prediction accuracy differs as a function of exogenous variables related to port characteristics and the maritime shipping context. Thus, the measuring problem of a priori container traffic predictability (i.e., the ability of methods to predict port traffic variables) arises. Academia suggests that this predictability may be related to entropy as a measure of the information provided. This paper analyses the Sample Entropy (SampEn), as an indicator of the complexity represented by the container traffic time-series data, comparing it with the error metrics provided by a statistical prediction and an artificial intelligent method. The Autoregressive Integrated Moving Average (ARIMA) and Back Propagation (BP) Neural Network are used to obtain the prediction performance of container port traffic in a data set composed of 20 Chinese ports and 5 selected international ports. Unexpectedly, the results indicate that the correlation between the SampEn and the prediction performance of the time series is weak. The complex ecosystem of the container shipping sector and the different port characteristics are suggested as the cause of this poor correspondence. Our findings help to understand the relationship between entropy and the predictability of time-series data in a sector (i.e. port traffic), which has not been explored before.
AB - Historical time-series data of container traffic in ports are characterised by strong randomness that causes the time-series prediction performance to vary significantly. In this sense, the prediction accuracy differs as a function of exogenous variables related to port characteristics and the maritime shipping context. Thus, the measuring problem of a priori container traffic predictability (i.e., the ability of methods to predict port traffic variables) arises. Academia suggests that this predictability may be related to entropy as a measure of the information provided. This paper analyses the Sample Entropy (SampEn), as an indicator of the complexity represented by the container traffic time-series data, comparing it with the error metrics provided by a statistical prediction and an artificial intelligent method. The Autoregressive Integrated Moving Average (ARIMA) and Back Propagation (BP) Neural Network are used to obtain the prediction performance of container port traffic in a data set composed of 20 Chinese ports and 5 selected international ports. Unexpectedly, the results indicate that the correlation between the SampEn and the prediction performance of the time series is weak. The complex ecosystem of the container shipping sector and the different port characteristics are suggested as the cause of this poor correspondence. Our findings help to understand the relationship between entropy and the predictability of time-series data in a sector (i.e. port traffic), which has not been explored before.
KW - Autoregressive Integrated Moving Average (ARIMA)
KW - Back Propagation (BP) neural network
KW - Container traffic
KW - Evolution cycle
KW - Sample entropy (SampEn)
UR - http://www.scopus.com/inward/record.url?scp=85149760989&partnerID=8YFLogxK
U2 - 10.1016/j.physa.2023.128483
DO - 10.1016/j.physa.2023.128483
M3 - Journal article
AN - SCOPUS:85149760989
SN - 0378-4371
VL - 612
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
M1 - 128483
ER -