Forecasting international tourism demand: a local spatiotemporal model

Jiao, Xiaoying; Li, Gang; Chen, Jason Li

doi:10.1016/j.annals.2020.102937

Cited by 64 publications

(44 citation statements)

References 54 publications

(69 reference statements)

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“…Model-free AI methods can identify nonlinear relationships between input and output variables without a priori knowledge (Song et al , 2019). Popular AI techniques include artificial neural networks (ANNs), random forest and support vector machines, along with new developments such as deep learning and kernel extreme learning machines (Jiao et al , 2020; Law et al , 2019; Sun et al , 2019). Nevertheless, when lacking a theoretical foundation, the contributions and implications of AI analytical findings are limited.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Combined models generate a set of forecasts by using different models and then integrate them into a final summarized forecast (Song et al , 2019). A hybrid model is essentially a single model combining the features of two or more models (Song et al , 2019), such as the common practice of augmenting time-series models with explanatory variables such as spatial data (Jiao et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

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Forecasting daily attraction demand using big data from search engines and social media

Tian

Yang

Mao

et al. 2021

IJCHM

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Purpose This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media. Design/methodology/approach Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy. Findings Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error. Practical implications Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions. Originality/value This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Forecasting daily attraction demand using big data from search engines and social media

Tian

Yang

Mao

et al. 2021

IJCHM

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“…In addition, the economic development level of the tourist source area also affects the population's difference in the demand of tourism. If the tourist source area is economically developed, the population growth will greatly lead to the increase in tourism demand, while on the contrary, if the tourist source area is relatively backward economically, the contribution of population growth to tourism demand will be relatively small [10].…”

Section: E Relationship Between the Demand For Ice And Snowmentioning

confidence: 99%

Dynamic Forecasting Algorithm of Inbound Ice and Snow Tourism in China Based on Improved Deep Confidence Network

Zhao

Jiao

et al. 2021

Mobile Information Systems

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Ice and snow-based tourism is getting popular around the world and it is one of the major sources of revenue for a region with required facilities. According to a report by China Daily, China was expected to witness 230 million tourist visits in 2020-2021 with a total revenue generation surpassing 390 billion yuan. In order to promote the ice and snow tourism, proper arrangements such as accommodation, transport facility, and energy provision for heating and food need to be arranged as per the demand of the visitors at a certain period of time. A tourist prediction system can help in this regard for good estimation but considering the problems of traditional ice and snow tourism systems, specifically the prediction accuracy and long forecasting time, a dynamic forecasting algorithm for ice and snow inbound tourism based on an improved deep confidence network is proposed. The system analyzes the relationship between the demand for ice and snow inbound tourism and the level of national economic development, people’s living standards, demographic characteristics, traffic conditions, and tourism supply capacity. It then takes the influencing factors of ice and snow inbound tourism demand as sample data and arranges the sample data sequence. The inbound tourism demand dynamic prediction model uses an improved deep confidence network to learn and train the prediction model, input test data into the trained model, and output the dynamic prediction value of ice and snow inbound tourism demand in the output layer to obtain the prediction result. The simulation results show that the proposed algorithm has improved accuracy in predicting the demand of inbound tourism for ice and snow, whereas the forecasting time is reduced.

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“…W konstruowaniu globalnego, autoregresyjnego modelu przestrzenno-czasowego najważniejsza jest lokalizacja geograficzna gospodarza. Aby ocenić rolę odległości terytorialnej między krajami wysyłającymi i przyjmującymi podróżujących, zastosowano w nim macierze wag przestrzennych (Jiao, Li, Chen, 2020). Modele dynamicznego panelu przestrzennego określają także znaczenie położenia regionu (prowincji), łącznie z odległością od miast stołecznych (Yang, Zhang, 2019).…”

Section: Przegląd Literaturyunclassified

“…The role of the host geographic location in global Spatiotemporal Autoregressive model construction prevails. This uses Spatial Weights matrices to assess the territorial proximity/remoteness of countries supplying and receiving tourists (Jiao, Li, Chen, 2020). Dynamic Spatial Panel models also assess the impact of region (province) location, including distance from capital cities (Yang, Zhang, 2019).…”

Section: Introductionmentioning

confidence: 99%

The forecasting potential of adaptive models in tourism

Александрова

Аигина

Dombrovskaya

2021

Tour

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The article discusses forecasting as one of the special scientific research areas which contribute to the assessment of tourist activity development prospects, the identification of key tourism development factors and effective management decision criteria. The study provides an overview of modern research methods used in Russia and other countries for making forecasts in the field of tourism. It aims at assessing the predictive capabilities of adaptive modeling, not frequently used currently in tourism research, for the quantitative analysis of tourist flows using the example of Barcelona, a major urban tourist destination in the pre-pandemic period. An example of a forecast for tourist numbers based on adaptive models is proposed, one of the key indicators showing tourist region success which have proven successful in the study of processes with a dynamic but unstable character.

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Forecasting international tourism demand: a local spatiotemporal model

Cited by 64 publications

References 54 publications

Forecasting daily attraction demand using big data from search engines and social media

Forecasting daily attraction demand using big data from search engines and social media

Dynamic Forecasting Algorithm of Inbound Ice and Snow Tourism in China Based on Improved Deep Confidence Network

The forecasting potential of adaptive models in tourism

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