Xinyang Liu
Academic and research departments
School of Hospitality and Tourism Management, Centre for Competitiveness of the Visitor Economy.About
My research project
Ensemble learning method for tourism demand forecastingThis project proposes several new ensemble learning methods to further improving the forecasting performance of the existing models. The first step of this study focuses on "bagging" which is designed to increase model robustness. Further studies pay special attention to bagging's performance on different data categories and introduce another method called "boosting" into the tourism demand forecast.
This project proposes several new ensemble learning methods to further improving the forecasting performance of the existing models. The first step of this study focuses on "bagging" which is designed to increase model robustness. Further studies pay special attention to bagging's performance on different data categories and introduce another method called "boosting" into the tourism demand forecast.
Publications
Highlights
Liu, A., & Liu, X. (2022). The autoregressive distributed lag model. In Econometric Modelling and Forecasting of Tourism Demand (pp. 53-75). Routledge.
Song, H., Liu, A., Li, G., & Liu, X. (2021). Bayesian bootstrap aggregation for tourism demand forecasting. International journal of tourism research, 23(5), 914-927. https://doi.org/10.1002/jtr.2453
Liu, X., Liu, A., Chen, J. L., & Li, G. (2023). Impact of decomposition on time series bagging forecasting performance. Tourism Management, 97, 104725.
Time series bagging has been deemed an effective way to improve unstable modelling procedures and subsequent forecasting accuracy. However, the literature has paid little attention to decomposition in time series bagging. This study investigates the impacts of various decomposition methods on bagging forecasting performance. Eight popular decomposition approaches are incorporated into the time series bagging procedure to improve unstable modelling procedures, and the resulting bagging methods' forecasting performance is evaluated. Using the world's top 20 inbound destinations as an empirical case, this study generates one-to eight-step-ahead tourism forecasts and compares them against benchmarks, including non-bagged and seasonal naïve models. For short-term forecasts, bagging constructed via seasonal extraction in autoregressive integrated moving average time series decomposition outperforms other methods. An autocorrelation test shows that efficient decomposition reduces variance in bagging forecasts.