Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation

Chang, Chia‐Lin; Sriboonchitta, Songsak; Wiboonpongse, Aree

doi:10.1016/j.matcom.2008.09.006

Cited by 38 publications

(18 citation statements)

References 8 publications

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“…Al respecto, para el estudio y proyección de la demanda del turismo con series de tiempo, existen diversos trabajos de investigación, así para su modelamiento ARIMA están los trabajos de Hosking (1981), Chang et al, (2009), Loganathan & Ibrahim (2010), Lim & Mcaleer (1999), Peiris (2016), Reisen (1994), Nanthakumar, Subramaniam, & Kogid (2012), Greenidge (2001). Para los costos del turismo en modelos ARIMA, el trabajo de Psillakis, Alkiviadis, & Kanellopoulos (2009); utilizando la metodología ARIMA-GARCH están las investigaciones de Coshall (2009), Shareef & McAleer (2005); con modelos ARMA-GARCH multivariado se cita el trabajo de Chan, Lim, & McAleer (2005).…”

Section: Revisión De Literaturaunclassified

Modelamiento y proyección de la demanda de turismo internacional en Puno-Perú

Blanco

Hancco

2020

RBTUR

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A indústria do turismo no Peru gera aproximadamente 1.1 milhão de empregos e contribui com 3.3% do PIB, o que a torna uma de suas principais atividades econômicas, portanto o turismo não é mais apenas uma atividade comercial mas é uma ferramenta para o desenvolvimento da população peruana, especialmente nas regiões com alto índice de pobreza e muitas atrações turísticas como é o caso da região de Puno com uma taxa de pobreza de 24.2% localizada no sul do país e com muitas atrações históricas, naturais, cultural e gastronômico. O objetivo desta pesquisa é modelar a procura de turistas internacionais que visitam Puno utilizando a metodologia ARIMA de Box-Jenkins, para este estudo considera informações mensais de chegadas de turistas internacionais entre os anos 2003 e 2017. Finalmente, usando estatísticas MAPE, Z, R, Critério de Informação de Akaike (AIC) e Critério de Schwarz (SC) se encontrou ao modelo SARIMA (6, 1, 24)(1, 0, 1)12 como o mais eficiente para a modelação e previsão da procura do Turismo Internacional na região de Puno.

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Section: Revisión De Literaturaunclassified

Modelamiento y proyección de la demanda de turismo internacional en Puno-Perú

Blanco

Hancco

2020

RBTUR

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“…• In [24], it has been mentioned that the length of the time series for ARIMA model is recommended to be greater than or equal to 50 points. • The fact that this is the nearest p o wer of 2 (64=2 6 ) greater than 5 0 , which is required during the application of the wavelet denoising by wavelets (see [18]), and then, prediction will be done over horizons of power of 2.…”

Section: Algorithm Of Online Energy Management In Hevmentioning

confidence: 99%

Energy management for a fuel cell hybrid electrical vehicle

Ibrahim

Wimmer

Jemeï

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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In order to perform an energy management strategy in hybrid electrical vehicles containing fuel cells, based on a power supply linking ultra-capacitors, batteries and fuel cells, time series prediction based on wavelet transform and auto-regressive integrated moving average is proposed in this paper. By wavelet denoising, the noise is removed from a part of the signal, by the auto-regressive integrated moving average method; a modeling and a prediction are done and thanks to the wavelet transform, the different frequency bands existing in the signal are attributed to the different power sources on board. The low frequency signal is attributed to the fuel cell and/or the batteries and the high frequency signal to the UC. Simulation results show the efficiency of the proposed method.

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“…Meanwhile, studies on tourism demand in developing countries such as Thailand have received minor attention. Although, several studies attempted to model demand for Thailand's tourism (Chang, Sriboonchitta, & Wiboonpongse, 2009;Song, Witt, & Li, 2003;Vogt & Wittayakorn, 1998), none of these studies have considered the impact of crime and environmental pollution on tourism demand in the country. Kim and Wong (2006) demonstrated that tourism demand is directly affected by issues pertaining to security and health.…”

Section: Introductionmentioning

confidence: 99%

“…In our analysis, inbound tourists are decomposed into sub-country level instead of using the aggregated number of tourist arrival to Thailand. Unlike the past studies (Chang et al, 2009;Vogt & Wittayakorn, 1998), we focus on the top six country that visited Thailand during the period of study. These countries consisting of China, Malaysia, South Korea, Japan, Russia, and India are regarded as the major tousim market for Thai economy.…”

Section: Introductionmentioning

confidence: 99%

Modeling The Demand for inbound Tourism to Thailand: Panel Data Approach

Preedatham¹,

Vivatvanit²,

Suksonghong³

2018

Proceedings of the 2nd International Conference on Tourism, Gastronomy, and Tourist Destination (ICTGTD 2018)

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This paper models the inbound tourism demand of Thailand by focusing on six major tourism markets, including China, Malaysia, South Korea, Japan, Russia, and India. Several economics variables widely used in the demand modeling literature were adopted. Besides, additional variables pertaining to environmental and security aspect were considered and incorporated into our investigated model. The panel data of six countries during 2003 and 2015 were collected from various sources. In our analysis, the random effects model was appropriated with the data set. The coefficient estimates were computed using panel least square method. Our results revealed that the substitute price of tourism destinations as well as the number of crime cases statistically significant in explaining the variation in tourism demand for Thailand. The major tourism markets of Thailand are sensitive the price of tourism and more likely to choose destination with lower costs of stay. Meanwhile, security and safety is another factor that influences their decision-making in selecting visiting destination.

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Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation

Cited by 38 publications

References 8 publications

Modelamiento y proyección de la demanda de turismo internacional en Puno-Perú

Modelamiento y proyección de la demanda de turismo internacional en Puno-Perú

Energy management for a fuel cell hybrid electrical vehicle

Modeling The Demand for inbound Tourism to Thailand: Panel Data Approach

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