Spatial-temporal forecasting of tourism demand

The results indicate that spatial-temporal forecasting outperforms the a-spatial counterpart in terms of average forecasting error, and Auxiliary regression finds the relative error of spatial- Temporal forecasting to be lower in regions characterized by a stronger level of local spatial association.

Abstract

This study conducts spatial-temporal forecasting to predict inbound tourism demand in 29 Chinese provincial regions. Eight models are estimated among a-spatial models (autoregressive integrated moving average [ARIMA] model and unobserved component model [UCM]) and spatial-temporal models (dynamic spatial panel models and space-time autoregressive moving average [STARMA] models with different specifications of spatial weighting matrices). An ex-ante forecasting exercise is conducted with these models to compare their one-/two-step-ahead predictions. The results indicate that spatial-temporal forecasting outperforms the a-spatial counterpart in terms of average forecasting error. Auxiliary regression finds the relative error of spatial-temporal forecasting to be lower in regions characterized by a stronger level of local spatial association. Lastly, theoretical and practical implications are provided. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.