Gaussian processes for daily demand prediction in tourism planning

Tsang, Wai Kit; Benoit, Dries F.

doi:10.1002/for.2644

Cited by 26 publications

(16 citation statements)

References 78 publications

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“…Given the reservation and occupancy history records, ridge regression, kernel ridge regression, multilayer perceptron, and radial basis function networks are constructed to forecast the daily occupancy rate, and the good forecasting performance is obtained [20]; Aliyevetc. [8] established the hotel occupancy forecasting system model based on fuzzy C-means clustering algorithm; Tsang et al [21] proposed using gaussian process to forecast daily occupancy rate, they also constructed linear regression, ARIMA, support vector machine, random forest and other recently commonly used machine learning methods in the experimental stage. Results showed that the performance of machine learning is generally better than traditional time series models.…”

Section: Literature Reviewmentioning

confidence: 99%

A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

Wang

2021

Teh. vjesn.

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The accuracy of hotel demand forecasting is affected by factors such as the completeness of historical data and the maturity of models. Most of the existing methods are based on rich data, without considering that single hotels may only obtain sparse data. Therefore, a K-means group division and Long Short-Term Memory (LSTM) based method is proposed in this paper. Guest types are introduced into the forecasting to provide reference for hotel's further decision-making. Using an example of 1493 hotels in Europe, we divide hotel groups and forecast the flow of leisure and business guests. The experimental results show that, compared with the benchmark models, LSTM can improve the forecasting performance of hotel group; compared with single hotels, the forecasting of hotel groups can effectively avoid inaccuracy caused by sparse data. The results can provide necessary reference for hospitality to make decisions based on guest types.

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Section: Literature Reviewmentioning

confidence: 99%

A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

Wang

2021

Teh. vjesn.

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“…weather or politics [20] and hotel characteristics like location and star rating [21]. The approach proposed in [22] is worth to mention as the authors use not only nonlinear model but also employ automatic feature selection to introduce additional variables. Models based on historical data -e.g.…”

Section: Related Workmentioning

confidence: 99%

Improving Time-Series Demand Modeling in Hospitality Business by Analytics of Public Event Datasets

Кamola

Arabas

2020

IEEE Access

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Forecasting occupancy in hospitality business with autoregressive time-series models does not intercept occasional impact of public events. Our goal was to find appropriate datasets and enrich existing predictive models to account for rare and explicable demand surges. The paper proposes processing framework: data source types and formats, and forecast algorithms based on natural language processing. The study shows that classical models using word collocations outperform state of the art deep neural networks. Also, the collocations that turn out to be important, occupy certain locations in a graph that represents the natural language. The findings may result in yet improved forecasts, leading to smarter offer pricing and, finally, increased competitiveness in hospitality business. They may also serve public interest in areas like parking management or public transport planning. INDEX TERMS Time series analysis, recurrent neural networks, regression analysis, natural language processing, predictive models.

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“…Multi-layer perceptron networks, which are among the models of artificial neural networks (Claveria et al, 2015;Kon & Turner, 2005;Law, 2000;Law & Au, 1999), and deep learning methods (Law et al, 2019) are widely used in forecasting tourism demand. Moreover, support vector machine (Chen & Wang, 2007;Chen et al, 2015;Hong et al, 2011), a composite search index (Li et al, 2017), the fuzzy time series (Tsaur & Kuo, 2011;Wang, 2004), Gaussian processes (Tsang & Benoit, 2020) are used in forecasting tourism demand.…”

Section: Tourism Demand Forecastingmentioning

confidence: 99%

Artificial intelligence and robotic technologies in tourism and hospitality industry

Kiliçhan

YILMAZ>²

2020

Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi

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Artificial intelligence applications and robotic technologies, which are rapidly spreading and widely used throughout the world, are discussed by different disciplines in the literature. The field of tourism draws attention as one of the disciplines in which studies on these issues have been carried out in recent years. In this context, robots come to the fore in the application areas of the tourism sector. However, it is known that there are many artificial intelligence applications that are becoming widespread or likely to become widespread day by day in the tourism sector. From this point of view, in this conceptual study, firstly artificial intelligence applications and robotic technologies were evaluated, the development of these technologies was revealed, then the current technologies used in the tourism and hospitality industry were examined, and as a result, the future of these technologies in the tourism and hospitality industry was discussed. In this context, it can be said that this study, in which the current situation is revealed and sector-experienced writers make inferences for the future, is an important study that can contribute to the literature and industry practitioners.

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Gaussian processes for daily demand prediction in tourism planning

Cited by 26 publications

References 78 publications

A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

Improving Time-Series Demand Modeling in Hospitality Business by Analytics of Public Event Datasets

Artificial intelligence and robotic technologies in tourism and hospitality industry

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