Forecasting tourist arrivals using STL-XGBoost method

Abstract

Forecasting tourism demand in a timely manner is critical for ensuring the smooth operation of the tourism industry. Over time, time series models have been widely applied to estimate the number of tourists arriving. In this paper, we proposed a XGBoost model for tourism demand forecasting based on the STL seasonal decomposition. The first phase of our proposed model involves applying STL decomposition to preprocess the time series, separating it into two components: the seasonal and de-seasonal terms. During the second phase, the seasonal term is modeled and predicted with the Holt-Winters model. For the de-seasonal term, the ARIMA model is first employed to capture the residual part, Then, the XGBoost model is utilized to reconstruct both the de-seasonal term and its lag, along with the residual part obtained from the ARIMA model. By integrating the forecast outputs from both the Holt-Winters and XGBoost models, the final tourism demand predictions can be derived. The effectiveness of the proposed model is demonstrated using the tourist arrivals data in Macau from eight countries: United States, Germany, Malaysia, Philippines, India, Thailand, Italy and Korea (South Korea). The validation results indicate that the proposed model exhibits superior forecasting performance for time series data showing seasonality and trendency, simultaneously enhancing interpretability without increasing model complexity. The model outperforms five benchmark comparison models when assessed using the Symmetric Mean Absolute Percent Error (SMAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics.