A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application

Alekseev, K.P.G.; Seixas, J. M.

doi:10.1016/j.jairtraman.2008.08.008

Cited by 64 publications

(39 citation statements)

References 8 publications

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“…The stopping criterion was the mean square error (MSE) of the estimated demand with respect to the samples belonging to the validation set. The validation set was not used in adapting the weight vectors of the neural estimator, and was therefore able to detect over-fitting in the training phase (Alekseev and Seixas, 2009;Srisaeng et al, 2015).…”

Section: Training and Testing The Artificial Neural Networkmentioning

confidence: 99%

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The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

2018

IJTTE

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Abstract:In this paper an Artificial Neural Network (ANN) is proposed for predicting Australia's annual export air cargo demand. The modelling in the study was based on annual data from 1993 to 2016. The ANN model was developed using the input parameters of world real merchandise exports, world population growth, world jet fuel prices, world air cargo yields (proxy for air cargo costs), outbound flights from Australia, and Australian/ United States dollar exchange rate and two dummy variables, which controlled for the strong cyclical fluctuations in air cargo demand which occurred in 2003 and 2015. The artificial neural network (ANN) used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 8 inputs, 1 neuron in the hidden layer and 1 neuron in the output layer. The data was randomly divided into three data sets; training, testing and model validation. The best-fit model was selected according to four goodness-of-fit measures: mean absolute error (MAE), mean square error (MSE), root mean square errors (RMSE), and mean absolute percentage errors (MAPE). The highest R-value obtained from the ANN model is 0.97844. The results suggest that the ANN model is an efficient tool for predicting Australia's annual export air cargo demand.

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Section: Training and Testing The Artificial Neural Networkmentioning

confidence: 99%

“…The model was accepted if the difference was low enough (Garrido et al, 2014). The testing set simulates the forecasting of the samples (Alekseev and Seixas, 2009).…”

Section: Training and Testing The Artificial Neural Networkmentioning

confidence: 99%

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

2018

IJTTE

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“…For example, it has developed a model for the analysis of air passengers, where it was found that the neural processing goes beyond the traditional econometric approach and offers the generalization in the behavior of time series, even when not only small samples [10]. Also, we have developed new hybrids, such as those that combine the analysis of singular spectrum, based on the matching network fuzzy inference system and improved particle swarm optimization for predicting air traffic short-term approaches.…”

Section: Modeling Air Freigth Colombia and The Worldmentioning

confidence: 99%

Modeling Demand for Air Cargo in the Colombian Context

Loaiza¹,

Solano²,

Trujillo³

et al. 2017

Proceedings of the 2017 International Conference on Advanced Materials Science and Civil Engineering (AMSCE 2017)

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Abstract-In recent years the growth of air cargo has accelerated, necessitating the assessment of the challenges that this growth will in the future, in order to overcome them and continue contributing to the economic development of the country. Considering the above, this paper proposes several models to estimate demand for air cargo in Colombia, obtained through the use of methodologies such as linear regression and neural networks, which can be used to characterize the current demand and to forecast future demand scenarios given certain contexts to be set.

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“…This manual was originally developed in 1985 using traditional modelling techniques (Alekseev and Seixas 2009). Historically, multiple linear regression (MLR) models have generally been used to forecast airline passenger traffic demand (see, for example, Aderamo 2010;Ba-Fail et al 2000;Bhadra 2003;Kopsch 2012;Sivrikaya and Tunç 2013).…”

Section: Traditional Air Travel Demand Forecasting Approachesmentioning

confidence: 99%

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

Srisaeng

Baxter

Richardson³

et al. 2015

J.Aerosp. Technol. Manag.

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This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia's domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively.

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A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application

Cited by 64 publications

References 8 publications

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

Modeling Demand for Air Cargo in the Colombian Context

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

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