Apply Deep Learning Neural Network to Forecast Number of Tourists

Chang, Yu‐Wei; Tsai, Chih-Yung

doi:10.1109/waina.2017.125

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…Compared to other DL models, the long short-term memory (LSTM) exhibits unique advantages in terms of forecasting with sequence as inputs [23][24][25]. In the field of tourism, for example, Chang and Tsai [26] used the deep neural network (DNN) model based on official statistics to forecast Taiwan's tourist flow. There is no research on the hotel accommodation demand forecasting based upon such DL methods.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Zhang

Wang

et al. 2019

Sustainability

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Accurate forecasting of the hotel accommodation demands is extremely critical to the sustainable development of tourism-related industries. In view of the ever-increasing tourism data, this paper constructs a deep learning framework to handle the prediction problem in the hotel accommodation demands. Taking China's Hainan province as an empirical example, the internet search index is used from August 2008 to May 2019 to forecast the overnight passenger flows for hotels accommodation in Hainan Province, China. Forecasting results indicate that compared to benchmark models, the constructed forecasting method can effectively simulate dynamic characteristics of the overnight passenger flows for the hotel accommodation and significantly improve the forecasting performance of the model. Forecasting results can provide necessary references for decision-making in tourism-related industries, and this forecasting framework can also be extended to other similar complex time series forecasting problems.

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Section: Introductionmentioning

confidence: 99%

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Zhang

Wang

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…In 2017, [61] examined various methodologies, namely ANNs, locally deep SVMs, decision jungles, decision trees and boosted decision trees, to accurately forecast hotel booking cancellations. [62] compared the performance of Deep Learning, SVMs and ANNs for tourist number forecasting, finding that Deep Learning outperforms the other two methods in accuracy. [63] also compared the feasibility of Gaussian process regression against ANNs in a multipleinput multiple-output setting, finding that as the models' memory increases the forecasting performance of Gaussian process regression, though ANNs using RBF outperform Gaussian process regression for long-term forecasting.…”

Section: Tourism Demand Forecastingmentioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

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This research work presents a detailed survey about Computational Intelligence (CI) applied to various Hotel and Travel Industry areas. Currently, the hospitality industry's interest in data science is growing exponentially because of their expected margin of profit growth. In order to provide precise state of the art content, this survey analyzes more than 160 research works from which a detailed categorization and taxonomy have been produced. We have studied the different approaches on the various forecasting methods and subareas where CI is currently being used. This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches. A set of guidelines and recommendations for future research areas and promising applications are also presented in a final section.

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“…At present, some scholars also build deep learning models for demand prediction. For example, Chang and Tsai [24] addressed the problem faced by neural network and SVR and proposed the deep learning neural network to predict the tourist arrivals; the result showed that the deep learning applied neural network with feature selection attained the best testing accuracy. Although the deep learning network proposed in this research can carry out feature selection, its prediction accuracy needs to be improved.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Passenger Flow Prediction Method for Pricing Strategy and Hotel Operations

Wang

2021

Complexity

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Hospitality industry plays a crucial role in the development of tourism. Predicting the future demand of a hotel is a key step in the process of hotel revenue management. Hotel passenger flow prediction plays an important role in guiding the formulation of hotel pricing and operating strategies. On the one hand, hotel passenger flow prediction can provide decision support for hotel managers and effectively avoid the waste of hotel resources and loss of revenue caused by the loss of customers. On the other hand, it is the guarantee of the priority occupation of business opportunities by hotel enterprises, which can help hotel enterprises adjust their operation strategies reasonably to better adapt to the market situation. In addition, hotel passenger flow prediction is helpful to judge the overall operating condition of the hotel industry and assess the risk level of the hotel project to be built. Hotel passenger flow is affected by many factors, such as weather, environment, season, holidays, economy, and emergencies, and has the characteristics of complex nonlinear fluctuation. The existing demand predicting methods include linear methods and nonlinear methods. The linear prediction methods rely on the stability of environment and time series, so they cannot completely simulate the complex nonlinear fluctuations characteristics of hotel passenger flow. Traditional nonlinear prediction methods need to improve the prediction accuracy, and they are difficult to deal with the increasing data of hotel passenger flow. Based on the above analysis, this paper constructs a deep learning prediction model based on Long Short-Term Memory (LSTM) to predict the number of actual monthly arrival bookings. The number of actual monthly arrival bookings can reflect the actual monthly passenger flow of a hotel. The prediction model can effectively reduce the loss caused by cancellation or nonarrival of bookings due to various reasons and improve the hotel revenue. The experimental part of this paper is based on the booking demand dataset of a resort hotel in Portugal from July 1, 2015, to August 31, 2017. Artificial neural network (ANN) and support vector regression (SVR) are built as benchmark models to predict the number of actual monthly arrival bookings of this hotel. The experimental results show that, compared with the benchmark models, the LSTM model can effectively improve the prediction ability and provide necessary reference for the hotel's future pricing decision and operation mode arrangement.

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Apply Deep Learning Neural Network to Forecast Number of Tourists

Cited by 14 publications

References 24 publications

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

An Intelligent Passenger Flow Prediction Method for Pricing Strategy and Hotel Operations

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