Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach

TL;DR

This paper introduces an adaptive multiscale ensemble learning approach combining VMD and LSSVR for accurate seasonal and trend forecasting of tourist arrivals across different time horizons.

Contribution

The study develops a novel ensemble method that decomposes tourist data into components and applies specialized models, improving forecasting accuracy over existing benchmarks.

Findings

Achieves higher forecasting accuracy than benchmark models.

Effectively captures seasonality and volatility in tourist arrivals.

Demonstrates robustness across short, medium, and long-term forecasts.

Abstract

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.