Forecasting tourism demand for Thua Thien Hue based on artificial neural networks
Vol. 130 No. 5A (2021), Original Article
Vol. 130 No. 5A (2021)

Forecasting tourism demand for Thua Thien Hue based on artificial neural networks

Original Article
https://doi.org/10.26459/hueunijed.v130i5A.6152
Published 2021-04-05
Vo Viet Minh Nhat
Hue University, 1 Dien Bien Phu St., Hue, Vietnam
Le Van Hoa
School of Hospitality and Tourism, Hue University, 22 Lam Hoang St., Hue, Vietnam
Nguyen Thi Thuy Van
School of Hospitality and Tourism, Hue University, 22 Lam Hoang St., Hue, Vietnam
Hoang Thi Hue
School of Hospitality and Tourism, Hue University, 22 Lam Hoang St., Hue, Vietnam
PDF (Vietnamese)

Keywords

Tourism demand forecasting
time-series data
artificial neural networks
tourist arrivals to Thua Thien Hue Dự báo nhu cầu du lịch
dữ liệu chuỗi thời gian
mạng nơ-ron nhân tạo
dữ liệu du khách đến Thừa Thiên Huế

Abstract

Accurate tourism demand forecasting for a destination plays a vital role in advising policymakers to plan and devise strategies related to investments in facilities, infrastructure improvements and services development. There are many different approaches in tourism demand forecasting, in which the one based on time-series data has attracted the most attention due to the unstructured nature of the particular data type. Neural networks have been evaluated as a predictive method specifically suited to this type of unstructured data. This paper examines the usage of neural networks, including MLP, RBF and ELN, to forecast tourism demand using time-series data in Thua Thien Hue. The analysis and comparison based on simulation show that the RBF network gives the best forecast result with the lowest MSE, RMSE, MAE and MAPE. This result is not only consistent with previous studies but also further confirms that the spatial conversion from nonlinear to linear of the hidden layer makes RBF powerful for the non-structural data.

https://doi.org/10.26459/hueunijed.v130i5A.6152
PDF (Vietnamese)

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