Forecasting US overseas travelling with univariate and multivariate models

Apergis, Nicholas

doi:10.1002/for.2760

Cited by 6 publications

(3 citation statements)

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“…Air passenger traffic flow modelling and analysis have been studied in the past for many different regional markets (Blinova, 2007; Carson et al, 2011; Grubb & Mason, 2001). The modelling process employs classical time series approach (Grubb & Mason, 2001), econometric modelling (Fildes et al, 2011; Nicholas, 2021), neural networks (Tsai et al, 2005), decision tree (Cheng et al, 2014), or a combination of several modelling methods (Bao et al, 2012). Blinova (2007) demonstrates the applicability of neural networks for air passenger traffic flows' forecasting in Russia.…”

Section: Related Workmentioning

confidence: 99%

Forecasting air passenger travel: A case study of Norwegian aviation industry

Anupam,

Lawal

2023

Journal of Forecasting

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Accurate forecasting of airline passenger traffic is important for facilitating the effective management and planning of aviation resources. In this study, we explore the air passenger traffic in the Norwegian aviation industry by collecting the passenger flow data and the corresponding measurements of the weather conditions affecting the flow from the different airports in Norway. We then proposed nonlinear autoregressive with exogenous input (NARX) forecasting models to predict air passenger traffic in advance. The NARX models account for the nonlinearity and nonstationarity in the passenger flow and allow the accurate forecasting of air passenger traffic. We perform experiments to demonstrate the effectiveness of two variants of the NARX model and compare their performances against long short‐term memory (LSTM), a deep learning method. We show that the proposed NARX model achieves the best prediction accuracy compared to LSTM, which is considered as a state‐of‐the‐art approach for fitting sequential data.

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Section: Related Workmentioning

confidence: 99%

Forecasting air passenger travel: A case study of Norwegian aviation industry

Anupam,

Lawal

2023

Journal of Forecasting

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“…Driven by macroeconomics, the tertiary industry has achieved unprecedented development; according to the World Bank (https://www.shihang.org/zh/home), the value of trade in services as a percentage of the gross national product (GDP) repeatedly increased from 2010 to 2019, from 11.72% to 13.557%. Particularly, tourism is known as one of the most eye‐catching parts owing to the income generated by goods, services, taxes, and employment opportunities associated with the tertiary industry (Law et al, 2019; Li et al, 2018; Nicholas, 2021). Tourism volume is vital for tourism managers and practitioners as it is the basis for tourism products and all tourism‐related decisions (Li et al, 2018; Liang, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…shihang.org/zh/home), the value of trade in services as a percentage of the gross national product (GDP) repeatedly increased from 2010 to 2019, from 11.72% to 13.557%. Particularly, tourism is known as one of the most eye-catching parts owing to the income generated by goods, services, taxes, and employment opportunities associated with the tertiary industry (Law et al, 2019;Li et al, 2018;Nicholas, 2021).…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning approach for tourism volume forecasting with tourist search data

Zhang

Sun

et al. 2022

Journal of Tourism Research

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Tourism volume forecasting is the hot topic in tourism management, and deep learning techniques as the promising tool are becoming popular for capturing the characteristics of tourism volume data, which is reflected in two aspects: data dimension reduction (i.e., stacked autoencoders [SAE]) and model forecasting (i.e., bi-directional gated recurrent unit neural network [Bi-GRU]). With Hong Kong inbound arrivals as a case, this study has empirically verified that deep learning techniques can improve forecasting accuracy. Furthermore, the proposed approach (i.e., SAE-Bi-GRU) is significantly superior to benchmark models (i.e., PCA-Bi-GRU with Baidu index and Google trends).

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