A practitioners guide to time-series methods for tourism demand forecasting — a case study of Durban, South Africa

Burger, Christoph; Dohnal, Mirko; Kathrada, M.; Law, Rob

doi:10.1016/s0261-5177(00)00068-6

Cited by 206 publications

(119 citation statements)

References 6 publications

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“…This result is different from that of Burger et al (2001) who showed the ANN model forecasts better compared to the ARIMA model. In this study, the MAPE of the seasonal ARIMA (2,0,2)x(2,0,1) 12 model was 5.1% while Burger et al (2001) reported the MAPE of ARIMA of 11.3%. Cho (2003) found that the MAPE is between 8.24% and 44.52%, using the ARIMA analysis of the six regions.…”

Section: Discussioncontrasting

confidence: 99%

“…In this study, the MAPE of ARIMA model had the lowest value. Burger et al (2001) found that the time-series model is the non-seasonal ARIMA model. Cho (2003) obtained a seasonal ARIMA model, which analyzes the number of tourists who visit Hong Kong from six different countries and regions.…”

Section: Discussionmentioning

confidence: 99%

“…ANN predicted tourism demand for the 12 months with the MAPE of 10.11%, 10.32%, 8%, 9.32%, 13.32%, and 11.99%, respectively. Burger et al (2001) predicted the tourism demand for 12 months with the MAPE of 11%. In this study, BNP predicted tourism demand for 12 months with the MAPE of 10.96%, which is very close to the results of the two previous studies.…”

Section: Discussionmentioning

confidence: 99%

“…Burger, Dohnal, Kathrada and Law (2001) have used the monthly data and ARIMA, genetic regression (GMDH) group method of data handling, and the ANN method to model tourism demand and forecasting in South African. It was found that the error rate established by ANN model is the lowest.…”

Section: Literature Reviewmentioning

confidence: 99%

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Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan

Lin

Chen

Lee

2011

IJBA

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In the past few decades, international tourism has grown rapidly and has become a very interesting topic in tourism research. Taiwan, acting as a citizen in the global community, improved traveling facilities, and governments' strong promotion has drawn more and more visitors to visit Taiwan. This study tries to build the forecasting model of visitors to Taiwan using three commonly adopted ARIMA, artificial neural networks (ANNs), and multivariate adaptive regression splines (MARS). In order to evaluate the appropriateness of the proposed modeling approaches, the dataset of monthly visitors to Taiwan was used as the illustrative example. Analytic results demonstrated that ARIMA outperformed ANNs and MARS approaches in terms of RMSE, MAD, and MAPE and provided effective alternatives for forecasting tourism demand.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan

Lin

Chen

Lee

2011

IJBA

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“…Empirical evidence shows that ANNs generally outperform the classical time series and multiple regression models in tourism forecasting. For example, Burger et al (2001) showed that the ANN method was the best performing model over the naïve 1, decomposition, exponential smoothing, ARIMA, multiple regression and genetic regression models.…”

Section: Other Quantitative Modelsmentioning

confidence: 99%

Tourism demand modelling and forecasting—A review of recent research

2008

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This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there is no single model that consistently outperforms others models in all situations. Furthermore, this study identifies some new research directions, which include improving the forecasting accuracy through forecast combination; integrating both qualitative and quantitative forecasting approaches, tourism cycles and seasonality analysis, crisis impact assessment and risk forecasting.

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Could European marine conservation policy benefit from systematic conservation planning?

Giakoumi

Katsanevakis

Vassilopoulou

et al. 2012

Aquatic Conservation

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The Natura 2000 network of protected areas aims to assure the long‐term survival of Europe's most valuable and threatened species and habitats. Yet, evidence shows that the present network fails to represent effectively the biodiversity of the region. Priority areas for conservation of coastal and offshore biodiversity features in the Greek Ionian Sea were identified, based on the principles of systematic conservation planning (SCP). SCP is a transparent method for the design of MPA networks and is considered more efficient and successful in representing the biodiversity of a region. The prioritization software Marxan was used and three scenarios with different sets of targets for 17 (high and low priority) conservation features were produced. These scenarios explicitly took into account socio‐economic factors expressed as a single cost metric, weighting different economic sectors in proportion to their contribution to the GDP of the region. Then results were compared with the existing Natura 2000 sites in terms of goal achievement, area requirements, and cost. The solutions produced by the systematic approach demanded less area and lower cost to achieve the goals set, when the selection of all Natura 2000 sites was not forced. Existing Natura 2000 sites alone failed to achieve conservation goals for some EU priority and other important coastal and offshore habitats and species of the Mediterranean Sea. It is suggested that the use of systematic conservation planning and related computational tools could benefit the selection of European marine priority areas, especially in the context of ecosystem‐based marine spatial management. Copyright © 2012 John Wiley & Sons, Ltd.

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A practitioners guide to time-series methods for tourism demand forecasting — a case study of Durban, South Africa

Cited by 206 publications

References 6 publications

Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan

Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan

Tourism demand modelling and forecasting—A review of recent research

Could European marine conservation policy benefit from systematic conservation planning?

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