Forecasting Tourist Indicators in Greece: A Comparative Evaluation of Statistical and Deep Learning Models

Nikolina Christou () and Vassiliki Karioti ()
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Nikolina Christou: University of Patras, Department of Tourism Management
Vassiliki Karioti: University of Patras, Department of Tourism Management

A chapter in Synergizing Management, Culture, and Arts for Tourism Development - Vol. 1, 2026, pp 537-557 from Springer

Abstract: Abstract Accurate forecasting of tourism demand is an essential tool for sustainable development and strategic planning of the tourism sector. This study investigates the performance of SARIMA statistical models and LSTM and GRU deep learning models in forecasting two critical indicators: international air arrivals and bed occupancy rates in Greece. In the case of SARIMA models, two scenarios were applied: one includes the COVID-19 pandemic period, while the other excludes it. The results show that SARIMA models are suitable in stable seasonal conditions, while LSTM and GRU neural networks achieve superior performance in complex and nonlinear periods, such as crisis periods. The study highlights the importance of selecting an appropriate forecasting model depending on the nature of the data and suggests future directions for improving the accuracy of tourism forecasting.

Keywords: Time series; Forecasting; Neural networks; Innovation management; Tourism and development; Greece (search for similar items in EconPapers)
JEL-codes: C32 C45 C53 O31 Z32 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-17545-8_22

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DOI: 10.1007/978-3-032-17545-8_22

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