Application of fuzzy regression on air cargo volume forecast

Abstract

Air cargo, Freight forecasting, Fuzzy linear regression, Index of optimism,

Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modeling

“…The complex non-linear relationships between different variables were analyzed efficiently by combining the learning ability of artificial neural networks and the transparent nature of fuzzy logic. Tsung et al [5] predicted the volume of Taiwan air cargo exports applying a fuzzy regression model. GDP was used as the independent variable in this research.…”

“…Since this section is briefly extracted from Tsung et al [5], Zadeh [13] and Tanaka [14] papers can provide more information about fuzzy set theory, fuzzy sets and triangle fuzzy numbers.…”

“…=^h from summing up all of the triangular membership functions' spread of the sample estimators would be subject to Equations 7 and 8" [5]. Finally, the target function to minimize the total amounts of the spread with n sample estimators is defined as follows:…”

“…This section is also extracted briefly from Tsung et al [5]. The cut ais used to determine the unpredictability nature of different scenarios.…”

Improving demand forecasting in the air cargo handling industry: a case study

“…Thus, effective air cargo demand forecasting is vital, provided its influence over the conduction of a company's operations (Becker & Wald, 2010). There are some other authors (Chou et al, 2013;Chen et al, 2012;Suryani et al, 2012) that undertake research towards enhancing the forecasts accuracy of air cargo demand, mainly in the long-run, from a broader macroeconomic perspective, taking into consideration factors such as population, employment rates, incomes per capita, GDP, GNP, economic growth rates, etc. However, the root cause that prevents forecasts from being accurate in this industry, is simply the uncertain and volatile nature of air cargo demand (Totamane et al, 2012;Amaruchkul et al, 2011;Wu, 2011;Popescu et al, 2006).…”