Combined Hierarchical Tourist Arrival Forecasts for Great Zimbabwe National Monuments

Abstract

Precise tourism estimates for tourism destination sites are crucial for decision-making. The objective of the study is to model and project Great Zimbabwe National Monuments (GZNM) tourist arrivals by combining hierarchical tourism forecasts. The approach improves tourism forecasting accuracy. GZNM monthly tourist arrivals are grouped according to tourism sources. A logarithm transformation is applied to tame the volatile data. Forecasting accuracy of the Simple Average Combination Method (SACM) and three hierarchical forecasting approaches (top-down, bottom-up, and optimal combination) were compared. The SACM under Autoregressive Integrated Moving Average (ARIMA) outperformed the other models, according to Root Mean Square Error (RMSE) measure. SACM is used to combine future tourist arrivals for the following 60 months and show a slow increase in tourist arrivals at GZNM. The data used in modeling are outside the COVID-19 pandemic period. Tourism stakeholders are encouraged to adopt the SACM in future tourism projections as it improves forecasting accuracy. Tourism stakeholders could carefully strategise and plan a recovery and ensure improvement in the tourism sector beyond the COVID-19 pandemic period. The COVID-19 pandemic is significantly affecting the tourism industry, reducing tourist arrivals to zero in some cases. The study revealed a fresh line of inquiry into how combining projections can increase forecasting accuracy.

Keywords

• hierarchical forecasting
• combination forecasts
• tourist arrivals
• tourism destination