International Tourists Arrival to Thailand: Forecasting by Non-linear Model

Chaitip, Prasert; Chaiboonsri, Chukiat

doi:10.1016/s2212-5671(14)00691-1

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…Methods for measuring the levels of development of tourist accommodation in the regions are considered by Derkach and Mylashko (2020). Chaitip and Chaiboonsri (2014) proposed methods for forecasting tourist flows to Thailand using a nonlinear model. The use of a logistic growth regression model to predict the demand for travel services in Macau hotels has been proposed by Chu, F.L.…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Regional measurement of the hotel sector development of a tourist destination (on the example of Odessa region, Ukraine)

Nezdoyminov¹,

Baldzhy²,

Kniazhkovska³

2021

Journ. Geol., Geogr., and Geoec..

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In the conditions of transformation of the national economy and intensification of competition in the regional tourist markets, Ukrainian hotel business enterprises have an important problem related to increasing the efficiency of filling hotel places and attracting tourist flows. One of the ways to solve this problem is to form a management measures based on forecasting the length of stay of tourists in accommodation facilities, load levels of hotel enterprises in tourist destinations of Ukraine, planning the production of competitive hotel services that can meet the needs of foreign and domestic consumers, market demand. This problem is especially important in the context of overcoming the effects of the global COVID-19 pandemic. The purpose of the article is to identify trends in the hotel business in the tourist regions of Ukraine and outline the prospects that can be realized in post-crisis conditions. The study used a statistical method of trend analysis of the dynamics of the duration of tourists in hotels, which aims at the establishing patterns of changes in the levels of the studied indicators over time, to identify the main trend of this phenomenon. The example of Odessa region proved that the evaluation of the hotel business areas are the basis for predicting spatial and temporal relationships in the regional economy for the development of hotel business in other tourist destinations, increase their competitiveness and improve the marketing of tourist flows. According to the calculations, the annual decrease in the length of stay of tourists for the period 2021-2022 in hotel establishments of the Odessa region is forecasted. The experience of experimental verification of the proposed approach to forecasting the length of stay of tourists in hotel establishments shows that the proposed research methods can form modern tools for diagnosing the current state of regional markets for hotel services and making management decisions.

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Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Regional measurement of the hotel sector development of a tourist destination (on the example of Odessa region, Ukraine)

Nezdoyminov¹,

Baldzhy²,

Kniazhkovska³

2021

Journ. Geol., Geogr., and Geoec..

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“…Bootstrap, a data resampling method, has also been introduced in tourism demand forecasting and is often implemented with AR time series. Kim et al (2010) proposed the use of bias-corrected bootstrap in interval forecasting of AR time series, and in the study by Chaitip and Chaiboonsri (2014), AR-bootstrapping approach and AR-maximum entropy bootstrapping approach were used to model high seasonal and low seasonal time series data. Both the two bootstrapping approaches resampled the residuals until maximum bootstrapping time was achieved, whereas the AR-maximum entropy bootstrapping also tried to minimize the error term uncertainty of AR-bootstrapping model (Chaitip and Chaiboonsri, 2014).…”

Section: Time Series Modelsmentioning

confidence: 99%

“…Thus, the STAR model is able to capture the smooth transition of regimes driven by the nonlinear patterns of the time series. Another nonlinear model used in tourism demand forecasting is Markov switching VAR (MS-VAR) model, which has been used in the study by Chaitip and Chaiboonsri (2014) to forecast international arrivals in Thailand. The method classifies the data series into high seasonal state and low seasonal state.…”

Section: Time Series Modelsmentioning

confidence: 99%

Tourism forecasting: A review of methodological developments over the last decade

2018

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This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.

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“…The non-linear forecasting model was employed by Chaitip and Chaiboonsri (2014) despite on the published literature that showed concern about modelling the tourism demand with a linear model. Furthermore, the research done by Chen et al (2015) aimed to investigate the Korean inbound tourism cycle using the Markov regime-switching model proposed by Hamilton (1989).…”

Section: Literature Reviewmentioning

confidence: 99%

Oil Price and Fijian Tourism Cycle: A Markov Regime-Switching Model

Soh

Puah

Arip

et al. 2019

IJEEP

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The fluctuation of oil price tends to have adverse effect on the tourism industry of a nation. This paper investigates the dynamic changes of the inbound tourism market for Fiji and the driving forces of the Fijian tourism cycle. A set of fundamental determinants of tourism demand including international crude oil price has been utilized to predict the Fijian tourism cycle for the period of 2000-2017. The Markov regime-switching model identifies two distinct phases of the Fijian tourism cycle which are an expansion and a recession period. The filtered and smoothed probabilities signalled the Fijian tourism development significantly with the transition probabilities supported. The adequate dating evaluation can offer essential information for policymakers, tourism industry players and even the community in decision making for Fijian tourism to enhance the nation's development.

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International Tourists Arrival to Thailand: Forecasting by Non-linear Model

Cited by 12 publications

References 20 publications

Regional measurement of the hotel sector development of a tourist destination (on the example of Odessa region, Ukraine)

Regional measurement of the hotel sector development of a tourist destination (on the example of Odessa region, Ukraine)

Tourism forecasting: A review of methodological developments over the last decade

Oil Price and Fijian Tourism Cycle: A Markov Regime-Switching Model

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