Abstract
In this paper we propose a new technique to forecast tourism demand based on Independent Component Analysis. The proposed method uses Dynamic Embedding (DE) to transform the time series in a higher dimensional space, where Independent Component Analysis is performed to estimate the independent components (sources). Prediction is then applied using well known forecasting techniques based on ARIMA models on each independent component, and the estimated ICs are transformed back into the data space to estimate the prediction. Experiments conducted using real data of tourism demand showing the occupancy of all tourist accommodations (except from camping sites) of the Western Region of Greece, have proven the efficacy of the proposed forecasting method compared to well-known methods based on ARIMA models, for various prediction steps.
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Acknowledgements
This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: ARCHIMEDES III. Investing in knowledge society through the European Social Fund.
The data that involves the monthly occupancy of all tourist accommodations of both foreign and domestic tourists came from the official records of the Hellenic Statistical Authority (EL. STAT., www.statistics.gr).
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Koutras, A., Panagopoulos, A., Nikas, I.A. (2016). Predicting Tourism Demand in the Western Greece Region Using Independent Component Analysis. In: Katsoni, V., Stratigea, A. (eds) Tourism and Culture in the Age of Innovation. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-27528-4_25
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DOI: https://doi.org/10.1007/978-3-319-27528-4_25
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