1. Proceedings
  2. I3M
  3. 2021
  4. EMSS
  5. 10.46354/i3m.2021.emss.006

Forecasting Tourism Demand in Greece Using Time Series Forecasting

  • a Eleni Saltsidou ,
  • b Maria Drakaki 
  • a,b International Hellenic University, 14th km Thessaloniki -N.Moudania, Thessaloniki, GR-57001, Hellas (GR)
Cite as
Saltsidou E., Drakaki M. (2021). Forecasting Tourism Demand in Greece Using Time Series Forecasting. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 39-44. DOI: https://doi.org/10.46354/i3m.2021.emss.006

Abstract

Tourism is one of the most important industries of the Greek economy and is a key to economic growth. In 2019, 27.53 million tourists arrived in hotel and camping accommodations of the country. While these arrivals help the economic growth of the country, there is a need to meet the expectations of tourists in the context of the provided services. The tourism industry should make investments in the infrastructure of the destinations in order to accommodate and manage the tourist flows. These investments include transportation services, logistics, accommodation and health services. Therefore, different methods have used tourism industry data to come up with accurate forecasts of tourist arrivals. The research on tourism demand supports decision making bodies to develop and improve practices that contribute to tourism development. The main aim of the paper is to provide time series forecasting models of tourist arrivals in Greece in order to assist decision making bodies and stakeholders to develop and improve practices that contribute to tourism development. Moreover, this paper aims to contribute to the research field of tourism demand forecasting with a case study that concerns Greece. Historical data of tourist arrivals to the Ionian Islands, obtained from ELSTAT for the time period 2010-2018, have been used in the time series forecasting models used in this research.

References

  1. Abellana, D. P. M., Rivero, D. M. C., Aparente, M. E., & Rivero, A. (2020). Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario. Journal of Tourism Futures.
  2. Alikhani R., Torab S.A., Altay N. (2019). Strategic supplier selection under sustainability and risk criteria. International Journal of Production Economics, 208, 69–82.
  3. Álvarez-Díaz, M., González-Gómez, M., & Otero-Giráldez, M. S. (2019). Forecasting international tourism demand using a non-linear autoregressive neural network and genetic programming. Forecasting, 1(1), 90-106. 
  4. Bi, J. W., Liu, Y., & Li, H. (2020). Daily tourism volume forecasting for tourist attractions. Annals of Tourism Research, 83, 102923.
  5. Chu, F. L. (2008). Analyzing and forecasting tourism demand with ARAR algorithm. Tourism Management, 29(6), 1185-1196.
  6. Claveria, O., & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220-228.
  7. Du Preez, J., & Witt, S. F. (2003). Univariate versus multivariate time series forecasting: an application to international tourism demand. International Journal of Forecasting, 19(3), 435-451. ELSTAT (https://www.statistics.gr/) 
  8. Goh, C., & Law, R. (2011). The methodological progress of tourism demand forecasting: a review of related literature. Journal of Travel & Tourism Marketing, 28(3), 296-317.
  9. Kasimati, E., & Ioakeimidisb, P. (2019). Accessible tourism in Greece: What is the current status?. ASSOCIATE EDITOR, 1, 33-42.
  10. Peng, B., Song, H., & Crouch, G. I. (2014). A meta-analysis of international tourism demand forecasting and implications for practice. Tourism Management, 45, 181-193.
  11. Sigala, M. (2020). Tourism and COVID-19: impacts and implications for advancing and resetting industry and research. Journal of Business Research.
  12. Song, H., Li, G., Witt, S. F., & Athanasopoulos, G. (2011). Forecasting tourist arrivals using time-varying parameter structural time series models. International Journal of Forecasting, 27(3), 855-869.
  13. Xu, X., Law, R., Chen, W., & Tang, L. (2016). Forecasting tourism demand by extracting fuzzy Takagi–Sugeno rules from trained SVMs. CAAI Transactions on Intelligence Technology, 1(1), 30-42.