Forecasting Tourist Arrivals in Albania Using Time Series Models and Currency Fluctuations
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Keywords:
Prediction, Tourist, Time Series, Seasonality, Arima, ETSAbstract
This study aims to forecast tourist arrivals in Albania by analysing quarterly data from 2016 to
the end of 2024, with a particular focus on the role of currency fluctuations, specifically the Euro, which is
commonly used in conjunction with the Albanian Lek. While a number of economic indicators were
considered, including GDP and other foreign currencies, the only one that showed a substantial correlation
with tourist traffic was the Euro. To predict and project tourist flows, time series models were employed,
including Exponential Smoothing (ETS), ARIMA, TBATS, and a regression model where the independent
variable is Euro and ARIMA-modeled residuals. The forecasting performance of each model was evaluated
using standard accuracy metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and
Mean Absolute Percentage Error (MAPE). Results indicate that the ETS model outperformed all others,
achieving the lowest error rates. ARIMA and TBATS indicated higher error margins, and the regression
residual hybrid approach performed moderately. These findings highlight the explanatory power of the
Euro in mimicking tourist demand in Albania and signal ETS to be the optimal model for short-term
forecasting in this regard. The study provides valuable guidelines for tourism planners and policymakers
seeking data-driven strategies for anticipating tourist flows.
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