Complete ensemble empirical mode decomposition with adaptive noise integrating feedforward neural network for tourist arrival forecasting

Abstract

The tourism sector has an important role in helping the income of a region, especially for economic development and opportunities to expand employment. However, the trend tourist arrival to these tourist attractions has decreased since the COVID-19 pandemic. The government enforces a new normal policy to reopen tourist attractions by implementing health protocols. Local governments and tourism managers need forecasting of tourist arrivals to help plan the tourism sector in the future and anticipate an increase in tourist arrival. Most tourist arrivals are influenced by several factors, such as: seasonality, politics, disasters, crises, and other important events. One method to accommodate these factors is using Ensemble Empirical Mode Decomposition (EEMD). However, EEMD still produces a mixing mode during decomposition. Complete Ensemble Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the weaknesses of EEMD. This research integrates CEEMDAN with Feedforward Neural Network (FNN) in generating forecasts. The experiment results show that the integration of CEEMDAN and FNN can produce good forecasting accuracy.