Forecasting city arrivals with Google Analytics

The ability of 10 Google Analytics website traffic indicators from the Viennese DMO website to predict actual tourist arrivals to Vienna is investigated, and combined forecasts based on Bates–Granger weights, on forecast encompassing tests, and on a novel fusion of these two perform best.

Abstract

The ability of 10 Google Analytics website traffic indicators from the Viennese DMO website to predict actual tourist arrivals to Vienna is investigated within the VAR model class. To prevent overparameterization, big data shrinkage methods are applied: Bayesian estimation of the VAR, reduction to a factor-augmented VAR, and application of Bayesian estimation to the FAVAR, the novel Bayesian FAVAR. Forecast accuracy results show that for shorter horizons (h = 1, 2 months ahead) a univariate benchmark performs best, while for longer horizons (h = 3, 6, 12) forecast combination methods that include the predictive information of Google Analytics perform best, notably combined forecasts based on Bates–Granger weights, on forecast encompassing tests, and on a novel fusion of these two.