Evaluation of the Forecast Models of Chinese Tourists to Thailand Based on Search Engine Attention: A Case Study of Baidu

Tang, Junjian

doi:10.1007/s11277-018-5413-2

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…These evaluation criteria are essentially measuring the dispersion degree between predicted and actual values (Chu, 2009; Kim and Malek, 2018). In general, previous studies usually choose two criteria (Sun et al, 2019; Tang, 2018), three criteria (Xu et al, 2019; Zhang et al, 2017), or four criteria (Li et al, 2018b) to reflect the forecast accuracy. In this study, the MAPE and RMSE are utilized as the evaluation criteria, which is consistent with previous research on tourism demand forecasting (Li et al, 2017; Li and Rob, 2019; Shahrabi et al, 2013; Song et al, 2011; Sun et al, 2019):…”

Section: Methodsmentioning

confidence: 99%

A novel BEMD-based method for forecasting tourist volume with search engine data

Tang

Zhang

et al. 2020

Tourism Economics

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As helpful big data, search engine data (SED) regarding tourism-related factors have currently been introduced to tourist volume prediction, but they have been shown to impact the tourism market on different timescales (or frequency band). This study develops a novel forecasting method using an emerging multiscale analysis—bivariate empirical mode decomposition (BEMD)—to investigate multiscale relationships. Three major steps are performed: (1) SED process to construct an informative index from sufficient SED using statistical analyses, (2) multiscale analysis to extract scale-aligned common factors from the bivariate data of tourist volumes and SED using BEMD, and (3) tourist volume prediction using an SED-based index. In the empirical study, the novel BEMD-based method with SED is used to forecast the tourist volume of Hainan in China, a global tourist attraction, and significantly outperforms both popular techniques (not considering SED or multiscales) and similar variants (considering SED or multiscales) in accuracy and robustness.

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Section: Methodsmentioning

confidence: 99%

A novel BEMD-based method for forecasting tourist volume with search engine data

Tang

Zhang

et al. 2020

Tourism Economics

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“…Based on the theory, tourism demand is affected by the income in the origin country, prices in the tourist destination, and a set of demand shifters [35]. Based on Tang [36] and Song, Witt and Li [18], we can model tourism demand using the following equation:…”

Section: Modeling Tourism Demand Using Average Tourism Spendingmentioning

confidence: 99%

The Determinants of Tourist Expenditure Per Capita in Thailand: Potential Implications for Sustainable Tourism

Chulaphan

Barahona

2021

Sustainability

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Tourism authorities in Thailand have consistently pursued profit-seeking mass tourism, resulting in the detriment of the natural resources in major tourist destinations. In response, sustainable tourism projects centered on preserving the environment have been established but neglect the financial needs of tour operators. The objective of this study was to investigate the determinants of tourist expenditure per capita in Thailand using a dataset consisting of 31 countries from 2010 to 2017. The analysis was based on an autoregressive distributed lag model (ARDL) and used a panel estimated generalized least square (ELGS). Generating such knowledge is essential for tourist authorities to develop profitable and sustainable tourism projects in tourist destinations whose natural resources have been affected by profit-seeking tourism. The tourism expenditure per capita is positively affected by word of mouth, income, and the rising prices in other major tourist destinations in Asia. However, it was negatively affected by relative levels of price and corruption. Sustainable tourism projects can be used to develop activities that will help distinguish Thailand from other tourism destinations in Asia. However, in implementing these sustainable tourism initiatives, the mark-up should be minimized to keep tourist prices in Thailand competitive.

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How is the Power of the Baidu Index for Forecasting Hotel Guest Arrivals? –A Case Study of Guilin

Liu

et al. 2022

Communications in Computer and Information Science

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Evaluation of the Forecast Models of Chinese Tourists to Thailand Based on Search Engine Attention: A Case Study of Baidu

Cited by 8 publications

References 17 publications

A novel BEMD-based method for forecasting tourist volume with search engine data

A novel BEMD-based method for forecasting tourist volume with search engine data

The Determinants of Tourist Expenditure Per Capita in Thailand: Potential Implications for Sustainable Tourism

How is the Power of the Baidu Index for Forecasting Hotel Guest Arrivals? –A Case Study of Guilin

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