Application of COEMD-S-SVR model in tourism demand forecasting and economic behavior analysis: The case of Sanya City

Fan, Guo‐Feng; Jin, Xiang-Ru; Hong, Wei‐Chiang

doi:10.1080/01605682.2021.1915192

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…To solve this problem, some studies turned to the decomposition-ensemble approach for tourism forecasting. For example, Fan et al used the empirical mode decomposition (EMD), the support vector regression, and the error factor adjustment to forecast the tourist ow of Sanya City, which provided an important basis for the economic development of Sanya [22]. Zhang et al proposed a decomposition ensemble method based on noise-assisted multivariate empirical mode decomposition (NA-MEMD)to prove that decomposition can further improve the prediction performance [23].…”

Section: 2models For Tourist Ow Predictionmentioning

confidence: 99%

A Multi-scale Combined Prediction on Tourist Flow: Does Internet Big Data Matter？

Liu,

Zhao,

Chen

et al. 2023

Preprint

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This article proposes a multi-scale combination prediction method for tourism traffic driven by internet big data. Firstly, process data on tourist traffic, Baidu index, and online reviews. Use Principal Component Analysis (PCA) to reduce the dimensionality of Baidu Index, and then use Snownlp to calculate the emotional value of each online comment. Secondly, a mixed multi-scale decomposition method is used to decompose tourism traffic and processed data, and sample entropy is used to reconstruct the decomposition results into high-frequency, low-frequency, and trend components. Thirdly, LSTM, BPNN, and SVR are used to predict high-frequency, low-frequency, and trend components, respectively, to obtain the predicted values under the three decomposition paths. The optimal weighted combination is used to obtain the final prediction result. Finally, the empirical analysis of tourism flow in Jiuzhaigou Valley,the result shows that the Internet big data can significantly improve the prediction effect of tourism flow.

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Section: 2models For Tourist Ow Predictionmentioning

confidence: 99%

A Multi-scale Combined Prediction on Tourist Flow: Does Internet Big Data Matter？

Liu,

Zhao,

Chen

et al. 2023

Preprint

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“…Vector autoregressive (VAR) models, such as Assaf et al (2018), Gunter and O ¨nder (2016) and Torraleja et al (2009), and error corrections and autoregressive distributed lag are widely used as well. AI-based methods, including neural networks (NNs) such as Claveria et al (2015Claveria et al ( , 2017 2011), Chen and Wang (2007) and Fang et al (2021), not only do not require any statistical assumptions, but also their strong feasibility and flexibility for nonlinear data have been clearly demonstrated (Bi et al, 2020).…”

Section: Tourism Forecastingmentioning

confidence: 99%

“…AI-based methods often require large-sized samples to achieve reasonable forecasting accuracy (Benedetto et al , 2021) as well. In recent years, mode decomposition has been applied to deal with nonlinear and nonstationary sequences by decomposing the original series into desired modes to reveal periodicities and trends in tourism demand; examples include Fang et al (2021), Li and Law (2020), Tang et al (2020), Xie et al (2020) and Xing et al (2022). However, while mode decomposition can extract several modes from the original sequence (Li et al , 2022), it is highly sensitive to sample size.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Vector autoregressive (VAR) models, such as Assaf et al (2018), Gunter and Önder (2016) and Torraleja et al (2009), and error corrections and autoregressive distributed lag are widely used as well. AI-based methods, including neural networks (NNs) such as Claveria et al (2015, 2017), Höpken et al (2020), Hu et al (2019), Silva et al (2019) and Xie et al (2020), deep learning such as Bi et al (2021), Kulshrestha et al (2020) and Zheng et al (2021), long short-term memory such as Bi et al (2020) and Wu et al (2021), and support vector regression (SVR) such as Chen (2011), Chen and Wang (2007) and Fang et al (2021), not only do not require any statistical assumptions, but also their strong feasibility and flexibility for nonlinear data have been clearly demonstrated (Bi et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

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The impact of Google Trends index and encompassing tests on forecast combinations in tourism

2022

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Purpose Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set. Design/methodology/approach Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set. Findings The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM. Practical implications With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting. Originality/value To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

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Forecasting tourism growth with State-Dependent Models

Guan

Silva

Hassani

et al. 2022

Annals of Tourism Research

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Application of COEMD-S-SVR model in tourism demand forecasting and economic behavior analysis: The case of Sanya City

Cited by 6 publications

References 29 publications

A Multi-scale Combined Prediction on Tourist Flow: Does Internet Big Data Matter？

A Multi-scale Combined Prediction on Tourist Flow: Does Internet Big Data Matter？

The impact of Google Trends index and encompassing tests on forecast combinations in tourism

Forecasting tourism growth with State-Dependent Models

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