  The accurate seasonal and trend forecasting of tourist arrivals is a very
challenging task. In the view of the importance of seasonal and trend
forecasting of tourist arrivals, and limited research work paid attention to
these previously. In this study, a new adaptive multiscale ensemble (AME)
learning approach incorporating variational mode decomposition (VMD) and least
square support vector regression (LSSVR) is developed for short-, medium-, and
long-term seasonal and trend forecasting of tourist arrivals. In the
formulation of our developed AME learning approach, the original tourist
arrivals series are first decomposed into the trend, seasonal and remainders
volatility components. Then, the ARIMA is used to forecast the trend component,
the SARIMA is used to forecast seasonal component with a 12-month cycle, while
the LSSVR is used to forecast remainder volatility components. Finally, the
forecasting results of the three components are aggregated to generate an
ensemble forecasting of tourist arrivals by the LSSVR based nonlinear ensemble
approach. Furthermore, a direct strategy is used to implement multi-step-ahead
forecasting. Taking two accuracy measures and the Diebold-Mariano test, the
empirical results demonstrate that our proposed AME learning approach can
achieve higher level and directional forecasting accuracy compared with other
benchmarks used in this study, indicating that our proposed approach is a
promising model for forecasting tourist arrivals with high seasonality and
volatility.

Applications;Machine Learning;Econometrics