In International journal of forecasting
We have been publishing real-time forecasts of confirmed cases and deaths for COVID-19 from mid-March 2020 onwards, published at www.doornik.com/COVID-19. These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative of short term developments, without requiring other assumptions of how the SARS-CoV-2 virus is spreading, or whether preventative policies are effective. As such they are complementary to forecasts from epidemiological models. The forecasts are based on extracting trends from windows of the data, applying machine learning, and then computing forecasts by applying some constraints to this flexible extracted trend. The methods have previously been applied to various other time series data and have performed well. They are also effective in this setting, providing better forecasts in the earlier stages than some epidemiological models.
Doornik Jurgen A, Castle Jennifer L, Hendry David F
Automatic forecasting, COVID-19, Epidemiology, Forecast averaging, Forecasting, Machine learning, Smoothing, Time series, Trend indicator saturation