Forecasts of Doctor Visits for Flu: Simple Conservative Methods Beat Google's Big Data Machine Learning Model

3 Pages Posted: 11 Feb 2023

See all articles by Kesten C. Green

Kesten C. Green

University of South Australia - UniSA Business; Ehrenberg-Bass Institute

Date Written: February 3, 2021


Purpose: Katsikopoulos et al. (2021) found that the recency heuristic's forecasts of week-ahead percentage of doctor visits associated with influenza symptoms-reduced forecast errors by nearly one-half compared to Google Flu Trends' machine learning model. This research note examined whether the accuracy of forecasts could be further improved by using another simple forecasting method (Green & Armstrong, 2015) that takes account of the observation that infection rates can trend, and did so in a conservative way (Armstrong, Green, and Graefe, 2015) by damping recent trends toward zero.

Methods: Out-of-sample forecasts from alternative models were compared using multiple error measures.

Findings: Damped trend models reduced absolute forecast errors by 13% relative to forecasts from the recency heuristic, by nearly 12% compared to forecasts from linear regression models, and by roughly 54% relative to Google Flu Trends forecasts.

Limitations: Rolling estimation of damping factors, multiple step-ahead forecasts, and forecasts of time series with many zero observations such as for smaller US states were not tested.

Implications:The findings provide further support for the superiority of the simple and conservative no-change and damped-trend (or combined) models to machine learning models for making time-series forecasts for complex uncertain situations.

Funding Information: I received no funding other than my university salary.

Declaration of Interests: No conflict of interest.

Keywords: complexity, forecast error, predictive validity, uncertianty, simplicity

JEL Classification: C1, C5, I1, Z18

Suggested Citation

Green, Kesten C., Forecasts of Doctor Visits for Flu: Simple Conservative Methods Beat Google's Big Data Machine Learning Model (February 3, 2021). Available at SSRN: or

Kesten C. Green (Contact Author)

University of South Australia - UniSA Business ( email )

GPO Box 2471
Adelaide, SA 5001
+61 8 83012 9097 (Phone)


Ehrenberg-Bass Institute ( email )



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