Research on combination forecast of port cargo throughput based on time series and causality analysis

Abstract

<p><strong>Purpose:</strong> 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.</p><p><strong>Design/methodology/approach:</strong> 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.</p><p><strong>Findings:</strong> 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.</p><p><strong>Research limitations/implications:</strong> 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.</p><p><strong>Practical implications:</strong> 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.</p><p><strong>Originality/value:</strong> This is the one of the study to improve the accuracy rate of the cargo throughput forecast with little information.</p

A Combining Forecasting Modeling and Its Application

“…The optimal conversion parameter λ * can be calculated according to the formula (8). The sum of square of forecasting error is minimized when λ * = 6.9419.…”

“…The forecasting error between forecasting result and actual demand are used as the evaluation standards of forecasting methods performance. There are many kinds of measure indexes of forecasting errors [8]. In this paper, four main measure indexes of forecasting error, which are the squares sum error (SSE), the mean square error (MSE), the mean absolute percentage error (MAPE) and the mean square percentage error (MSPE), are applied to comprehensively evaluate the forecasting accuracy of Bayesian combination forecasting model.…”

Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

“…Namely, high accuracy of the forecasts can essentially influence the appropriate strategy for port development, infrastructure-based investments, and efficient daily management [1]. On the other hand, inaccurate forecasts can lead to negative effects and financial losses that result in inadequate infrastructure investments and poorly chosen port strategy in the future, as well as improper decisions about the port upgrade or port redesign [2,3].…”

Spatiotemporal Characteristics of Urban Sprawl in Chinese Port Cities from 1979 to 2013

“…As Song (2013) indicated, the integration of the port and the city has become a major trend when constructing port cities in China [67]. Our intention here is to simply examine the relationship of port development and urban sprawl, while other studies have explored the development model of port and city combinations [68][69][70], and the methods relating the port cargo throughput with city GDP [71][72][73][74] in detail. The relationship implies the relatively close connection (R 2 is about 0.7) between the port development (represented by cargo throughput) and urban sprawl in the coastal zone.…”