A Hybrid Approach on Tourism Demand Forecasting

Nor, Maria Elena; Nurul, A. I M; Rusiman, Mohd Saifullah

doi:10.1088/1742-6596/995/1/012034

Cited by 18 publications

(10 citation statements)

References 24 publications

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“…Experiments combining ANN models with traditional timeseries approaches have emerged as an important focus of tourism demand forecasting studies. For instance, Nor, Nurul and Rusiman (2018) propose to combine the Box-Jenkins and ANN models, and Chen (2011) combines linear models (such as the naïve, ES or ARIMA models) with nonlinear AI models (such as back-propagation neural networks or SVRs) to evaluate the models' turning points in forecasting performance.…”

Section: [Insert Figure 1 About Here] Ai-based Modelsmentioning

confidence: 99%

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

Song

Qiu

Park

2019

Annals of Tourism Research

375

215

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Section: [Insert Figure 1 About Here] Ai-based Modelsmentioning

confidence: 99%

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

Song

Qiu

Park

2019

Annals of Tourism Research

375

215

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“…This study, however, starts with Model 1 and gradually adds other macroeconomic factors and Baidu index factors. Firstly, based on Song et al (2011), Chatziantoniou et al (2016), Wu et al (2017) and Nor et al (2018), this study incorporates the other macroeconomic variablesthat is economic policy uncertainty (EPU), consumer price differentials (CPDs), consumer confidence index (CCI), consumer price index (CPI) and the logarithmic form of the lag value of visitor arrivals (VA lags ) into Model 1 to construct Model 2. The purpose of this process is to analyse whether adding different variables contributes to the nowcasting performance of the tourism demand model.…”

Section: Nowcasting Processmentioning

confidence: 99%

Measuring tourism demand nowcasting performance using a monotonicity test

et al. 2022

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Tourism demand nowcasting is generally carried out using econometric models that incorporate either macroeconomic variables or search query data as explanatory variables. Nowcasting model accuracy is normally evaluated by traditional loss functions. This study proposes a novel statistical method, the monotonicity test, to assess whether the nowcasting errors obtained from the ordinary least squares, generalised dynamic factor model and generalised dynamic factor model combined with mixed data sampling model are monotonically decreasing when new data on explanatory variables become available, based on the mixed frequency data between 1 January 2011 and 31 December 2019. The results of the empirical analysis show that nowcasts generated results based on two data sources combined are superior to that based on a single data source. Compared with traditional loss functions, the monotonicity test leads to a more objective and convincing nowcasting model performance. This study is the first attempt to evaluate tourism demand nowcasting performance using a monotonicity test.

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“…autoregressive (ARIMA, SARIMA -seasonal ARIMA) or even their combination with nonlinear e.g. neural networks [23] are quite successful in modeling influence of endogenous factors while they lack ability to explain how the environment influences the process. In particular, an environmental factor that attracts our interest are extraordinary events, especially so-called mega events [24] -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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A Hybrid Approach on Tourism Demand Forecasting

Cited by 18 publications

References 24 publications

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

Measuring tourism demand nowcasting performance using a monotonicity test

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

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