Baidu index-based forecast of daily tourist arrivals through rescaled range analysis, support vector regression, and autoregressive integrated moving average

Yao, Lifei; Ma, Ruimin; Wang, Hua

doi:10.1016/j.aej.2020.08.037

Cited by 26 publications

(20 citation statements)

References 23 publications

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“…As of recently, the method of RRA is still being utilized in analyzing medical data [30], geological data [31][32][33], internet traffic data [34,35], and solar activity data [36]. Additionally, it is also utilized in the field of engineering [37].…”

Section: Rescaled Range Analysis (Rra)mentioning

confidence: 99%

Identification of Fractal Properties in Geomagnetic Data of Southeast Asian Region during Various Solar Activity Levels

et al. 2021

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The fractal properties of geomagnetic northward component data (H-component) in the equatorial region during various phases of solar activity over Southeast Asia were investigated and then quantified using the parameter of the Hurst exponent (H). This study began with the identification of existence of spectral peaks and scaling properties in international quiet day H-component data which were measured during three levels of solar activity: low, intermediate, and high. Then, various cases of quiet and disturbed days during different solar activity levels were analyzed using the method that performed the best in the preceding part. In all the years analyzed, multifractal scaling and spectral peaks exist, signifying that the data have fractal properties and that there are external factors driving the fluctuations of geomagnetic activity other than solar activity. The analysis of various cases of quiet and disturbed days generally showed that quiet days had anti-persistence tendencies (H < 0.5) while disturbed days had persistence tendencies (H > 0.5)—generally a higher level of Hurst exponent compared to quiet days. As for long-term quiet day H-component data, it had a Hurst exponent value that was near H ≃ 0.50, while the long-term disturbed day H-component data showed higher values than that of the quiet day.

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Section: Rescaled Range Analysis (Rra)mentioning

confidence: 99%

Identification of Fractal Properties in Geomagnetic Data of Southeast Asian Region during Various Solar Activity Levels

et al. 2021

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“…In the existing studies, there is a strong correlation between the tourist passenger demand and the tourist passenger traffic of the same period in previous years [14], so this paper incorporates the passenger transport of the same period in previous years into the model, which is helpful to improve the prediction accuracy. (4) Social network data: Studies have shown that changes in public sentiment in Twitter always affect the Dow Jones Industrial Index (DIJA) after 2 to 3 days [20].…”

Section: A Selection Of Variablesmentioning

confidence: 99%

“…Zhang [13] used search engine data and historical passenger flow data to predict tourism demand in scenic spots. Yao [14] used the Baidu Index to predict the daily passenger flow of tourist attractions. Li [15] combined web search data with weather and holidays to predict the daily tourist flow.…”

Section: Introductionmentioning

confidence: 99%

A Forecast Model of Tourism Demand Driven by Social Network Data

et al. 2021

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To improve the forecasting accuracy of tourism demand through forecasting model and data sources, this paper takes the social network data as an entry point, and collects the social network data by the web crawler, then quantifies the data based on the sentiment analysis of the BERT model. This paper uses structured variables such as social network data, weather, holidays, etc. to build a tourism demand forecasting model based on Gradient Boosting Regression Trees. At last, take Huang Shan as example, use actual statistics of passenger terminal and social network data to do an empirical analysis of Huang Shan tourism demand forecasting. Compared with the existing model and introduce ablation study to verify the effectiveness of the considered factors. The result shows that the model based on social network data has improved the forecasting accuracy from the existing ones, ablation study shows social network data helps to improve the accuracy of tourism demand forecasting.

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“…Data-driven models can be divided into single (monomer) and hybrid models. Monomer models include the linear model [12] , the support vector regression (SVR) prediction model [13][14][15] , and the neural network runoff prediction mode [16][17][18][19] . Monomer models often have complex calculation structures, low generalisation capabilities, and low operating efficiency.…”

Section: Introductionmentioning

confidence: 99%

Runoff Prediction and Analysis Based on Improved CEEMDAN-OS-QR-ELM

et al. 2021

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To solve the problems of low prediction accuracy, poor stability, and low calculation efficiency in runoff forecasting, this study develops an extreme learning machine (ELM) model based on improved complete ensemble empirical mode decomposition adaptive noise (CEEMDAN). The model first uses orthogonal triangular decomposition (QR) to redefine the ELM hidden layer output to construct the orthogonal triangular ELM (QR-ELM). It then introduces the online sequence mechanism (OS) into the QR-ELM model to construct online sequence OR-ELM (OS-QR-ELM), which can effectively improve the efficiency of the ELM models. Then, it uses the correlation wave extension method to extend both ends of the original signal to solve the end effect of CEEMDAN. Finally, it integrates CEEMDAN and OS-QR-ELM combined with parallel computing ideas to construct a parallel CEEMDAN-OS-QR-ELM runoff prediction method. The experimental results show that compared with the support vector regression method combined with empirical mode decomposition, the accuracy and reliability of the parallel CEEMDAN-OS-QR-ELM model was 1.09% and 8% higher, respectively. Compared with the ELM model, the efficiency of OS-QR-ELM was 5%.

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Baidu index-based forecast of daily tourist arrivals through rescaled range analysis, support vector regression, and autoregressive integrated moving average

Cited by 26 publications

References 23 publications

Identification of Fractal Properties in Geomagnetic Data of Southeast Asian Region during Various Solar Activity Levels

Identification of Fractal Properties in Geomagnetic Data of Southeast Asian Region during Various Solar Activity Levels

A Forecast Model of Tourism Demand Driven by Social Network Data

Runoff Prediction and Analysis Based on Improved CEEMDAN-OS-QR-ELM

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