Forecasting tourist arrivals using denoising and potential factors

Li, Cheng; Ge, Peng; Liu, Zhusheng; Zheng, Weimin

doi:10.1016/j.annals.2020.102943

Cited by 50 publications

(25 citation statements)

References 56 publications

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“…Due to international, regional, and local travel restrictions, the COVID-19 epidemic also had a serious adverse effect on Taiwan’s tourism industry, which represents one of the top service sectors for the island. Unlike other business sectors, tourism products tend to be especially perishable (Law et al, 2019; Li et al, 2020; Saayman and de Klerk, 2019), so there is a considerable loss in tourism revenue from cancellations of, for example, hotel rooms, airline seats, dining reservations, and banquet halls. To mitigate the effects of the COVID-19 crisis in the tourism sector and accelerate recovery, the Taiwan government designed subsidies and financial support of up to US$748 million for local tourism-related companies in the wake of the economic impact (Taiwan Tourism News Media, 2020).…”

Section: Introductionmentioning

confidence: 99%

Revisit stock price bubbles in the COVID-19 period: Further evidence from Taiwan’s and Mainland China’s tourism industries

2021

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This study applies both the sup augmented Dickey–Fuller (SADF) and the generalized SADF tests to investigate whether bubbles exist in the Taiwan’s and Mainland China’s tourism stock markets, focusing on the COVID-19 period. We use the weekly stock price index during the periods of January 4, 2000 to April 27, 2020 in Taiwan and August 13, 2007 to April 27, 2020 in Mainland China, respectively. Empirical results demonstrate the existence of bubbles in the Taiwan tourism stock market during some subsample periods, including the recent COVID-19 outbreak. In contrast with our findings concerning Taiwan, our empirical results found that the COVID-19 outbreak had no impact on the tourism stock market of Mainland China. Especially important, we found that the fourth bubble in the Taiwan stock market began from March 23, 2020, and grew gradually, bursting on March 30, 2020. Our results have important policy implications for investors who attempt to invest in the tourism stock market.

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Section: Introductionmentioning

confidence: 99%

Revisit stock price bubbles in the COVID-19 period: Further evidence from Taiwan’s and Mainland China’s tourism industries

2021

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“…Then, based on the obtained components, the forecasting model can be built and the further tourism demand can be predicted. The most commonly used decomposition models in tourism demand forecasting mainly include SSA (Silva et al 2019; Wu, Song, and Shen 2017), EMD (C. Li et al 2020), STL (Theodosiou 2011), etc.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Daily Tourism Demand for Tourist Attractions with Big Data: An Ensemble Deep Learning Method

et al. 2021

Journal of Travel Research

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Accurate forecasting of daily tourism demand is a meaningful and challenging task, but studies on this issue are scarce. To address this issue, multisource time series data, relating to past tourist volumes, web search information, daily weather conditions, and the dates of public holidays, are selected as the forecasting variables. To fully capture the relationship between these forecasting variables and actual tourism demand automatically, an ensemble of long short-term memory (LSTM) networks is proposed with a correlation-based predictor selection (CPS) algorithm. The effectiveness of the proposed method is verified in daily tourism demand forecasting for the Huangshan Mountain Area, benchmarked against 11 forecasting methods. This study contributes to the literature by (1) introducing the use of big data in daily tourism demand forecasting, (2) proposing an ensemble of LSTM networks for daily tourism demand forecasting, and (3) providing an effective predictor selection algorithm in ensemble learning.

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“…Examples include the back propagation neural network (NN) (Li et al, 2018;Hu and Song, 2019), support vector regression (Sun et al, 2019;Li et al, 2020b), random forest (RF) (Li et al, 2020b) and deep learning model (Law et al, 2019). Hybrid artificial intelligence models in which search query volumes are taken as predictor variables have also been adopted to forecast tourism demand (Li et al, 2018;Wen et al, 2019;Li and Law, 2020;Li et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?

Xiao

2021

IJCHM

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Purpose While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting. Design/methodology/approach Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting. Findings Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal. Practical implications Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management. Originality/value This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.

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Forecasting tourist arrivals using denoising and potential factors

Cited by 50 publications

References 56 publications

Revisit stock price bubbles in the COVID-19 period: Further evidence from Taiwan’s and Mainland China’s tourism industries

Revisit stock price bubbles in the COVID-19 period: Further evidence from Taiwan’s and Mainland China’s tourism industries

Forecasting Daily Tourism Demand for Tourist Attractions with Big Data: An Ensemble Deep Learning Method

Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?

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