Research on combination forecast of port cargo throughput based on time series and causality analysis
Abstract: The purpose of this
paper is to develop a combined model composed of grey-forecast model and
Logistic-growth-curve model to improve the accuracy of forecast model of cargo
throughput for the port. The authors also use the existing data of a current
port to verify the validity of the combined model.
Design/methodology/approach: A literature review is undertaken to find
the appropriate forecast model of cargo throughput for the port. Through
researching the related forecast model, the authors put together the individual
models which are significant to study further. Finally, the authors combine two
individual models (grey-forecast model and Logistic-growth-curve model) into
one combined model to forecast the port cargo throughput, and use the model to
a physical port in China to testify the validity of the model.
Findings: Test by the perceptional data of cargo throughput in the
physical port, the results show that the combined model can obtain relatively
higher forecast accuracy when it is not easy to find more information.
Furthermore, the forecast made by the combined model are more accurate than any
of the individual ones.
Research limitations/implications: The study provided a new combined
forecast model of cargo throughput with a relatively less information to
improve the accuracy rate of the forecast. The limitation of the model is that
it requires the cargo throughput of the port have an S-shaped change trend.
Practical implications: This model is not limited by external conditions
such as geographical, cultural. This model predicted the port cargo throughput
of one real port in China in 2015, which provided some instructive guidance for
the port development.
Originality/value: This is the one of the study to improve the accuracy
rate of the cargo throughput forecast with little information.
Keywords: cargo throughput,
combined forecast model, Logistic growth curve model, Gray forecast model
Author: Chi Zhang, Lei Huang,
Zhichao Zhao
Journal Code: jptindustrigg130073
