Big Data: Forecasting and Control for Tourism Demand

Reina, Miguel Ángel Ruiz

doi:10.1007/978-3-030-56219-9_18

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Furthermore, the causality models of the time series have a forecasting and control purpose that, in our model, was measured by applying the Theil inequality index. In the example of Spanish tourism, it should be noted that for the training period, i.e., from January to 2018 to December 2018, the results of the Theil inequality index were very close to zero [70].…”

Section: Discussionmentioning

confidence: 92%

Entropy Method for Decision-Making: Uncertainty Cycles in Tourism Demand

Reina

2021

Entropy

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A new methodology is presented for measuring, classifying and predicting the cycles of uncertainty that occur in temporary decision-making in the tourist accommodation market (apartments and hotels). Special attention is paid to the role of entropy and cycles in the process under the Adaptive Markets Hypothesis. The work scheme analyses random cycles from time to time, and in the frequency domain, the linear and nonlinear causality relationships between variables are studied. The period analysed is from January 2005 to December 2018; the following empirical results stand out: (1) On longer scales, the periodicity of the uncertainty of decision-making is between 6 and 12 months, respectively, for all the nationalities described. (2) The elasticity of demand for tourist apartments is approximately 1% due to changes in demand for tourist hotels. (3) The elasticity of the uncertainty factor is highly correlated with the country of origin of tourists visiting Spain. For example, it has been empirically shown that increases of 1% in uncertainty cause increases in the demand for apartments of 2.12% (worldwide), 3.05% (UK), 1.91% (Germany), 1.78% (France), 7.21% (Ireland), 3.61% (The Netherlands) respectively. This modelling has an explanatory capacity of 99% in all the models analysed.

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Section: Discussionmentioning

confidence: 92%

Entropy Method for Decision-Making: Uncertainty Cycles in Tourism Demand

Reina

2021

Entropy

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“…The datasets generated during and analysed during the current study are available in the repository (https://drive.google.com/file/d/1TP63q-1IFW0_oRJHXBDBFJxMP ANOTc9b/view?usp=sharing, accessed on 23 June 2021) [1][2][3][4][5][6][7].…”

Section: Institutional Review Board Statement: Not Applicablementioning

confidence: 99%

Cycles and Uncertainty: Applications in the Tourist Accommodation Market

Reina

2021

The 7th International Conference on Time Series and Forecasting

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In the socio-economic field, it is not surprising that decision-making is based on asymmetric information. Economic agents make decisions to forecast in primary and secondary industries related to the tourism sector. This study aims to provide knowledge in situations of asymmetric information with increasing randomness using time series for tourism accommodation markets. We are trying to solve the question of how consumers exchange their preferences for tourist accommodation between tourist apartments and hotel accommodation in Spain. The emergence of the sharing economy concept has emerged as a competitor to the traditional hotel accommodation in the tourist market. To do this, we will develop a theoretical framework to measure situations of uncertainty and their temporal evolution. Information Theory (IT) is the central axis of the study, particularly the concept of entropy. The Shannon entropy (SE) concept is a static measure of information. This work proposes to model the temporal arrangement of SE to discover the behaviors of the systems. The study in the domain of time and frequency allows us to understand the cycles of uncertainty between systems. To apply the theoretical framework, we will work with data from official Spanish sources for tourist accommodation from January 2008 to December 2019. The results of the empirical analysis show the decision changes of economic agents according to a seasonal pattern. Consumers have new accommodation options, and the answer we get from this work is that consumers have different preferences depending on seasonality. The use of SE allows us to make better predictions compared to SARIMA models, the traditional modelling of seasonal dummy variables, and VAR models. The results of the Matrix U1 Theil verify this hypothesis. The theoretical framework and empirical analysis find an answer to asymmetric information. The implications of this work contribute to the field of social sciences related to the tourism sector, in particular to thermodynamics, statistical mechanics, and IT. The modelling of uncertainty allows for the forecasting and control of accommodation tourist markets in random situations. The applications of this study can be tested in other areas of the economy such as finance, transportation, or investment.

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Digital Economy Oriented Tourism Industry Data Analysis in Semantic IoT

Wang,

Parameshachari,

Miao

2024

Internet Technology Letters

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The digital economy has revolutionized the tourism industry by integrating Semantic Internet of Things (IoT) technologies for enhanced data analysis. This paper proposes a comprehensive method for analyzing tourism flow using Semantic IoT. The method combines semantic data processing with advanced machine learning techniques, specifically stacked autoencoders and long short‐term memory (LSTM) models, to understand and predict changes in passenger flow. The approach addresses challenges of high dimensionality and complex temporal characteristics in tourism data, offering significant support for tourism management, improving operational efficiency, and enhancing the visitor experience. Experimental results demonstrate the effectiveness of the proposed method in analyzing and predicting tourism passenger flow data, providing accurate and insightful trends that are crucial for decision‐making in the tourism industry.

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Big Data: Forecasting and Control for Tourism Demand

Cited by 4 publications

References 30 publications

Entropy Method for Decision-Making: Uncertainty Cycles in Tourism Demand

Entropy Method for Decision-Making: Uncertainty Cycles in Tourism Demand

Cycles and Uncertainty: Applications in the Tourist Accommodation Market

Digital Economy Oriented Tourism Industry Data Analysis in Semantic IoT

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