A Neural network enhanced hidden Markov model for tourism demand forecasting

Abstract

In recent years, tourism demand forecasting has attracted more interests not only in tourism area but in data science field. In this study, we follow the previous relevant data science literatures and propose a new neural network enhanced hidden Markovian structural time series model (NehM-STSM). This model takes a multiplicative error structure of a trend and a seasonal element. The trend is modelled by an artificial neural network while the seasonal element is captured by a tailor-made hidden Markovian model with four components: a persistence replicative cycle, a jump component capturing an unexpected event, an amplitude component reflecting the event strength and a random error term. The empirical research is conducted using US incoming tourism data from twelve major source countries across January 1996–September 2017. The proposed NehM-STSM achieves a better performance than the chosen benchmark models for two error measures and most forecasting horizons.