Publication: Baseline accuracy of forecasting COVID-19 cases in Moscow region on a year in retrospect using basic statistical and machine learning methods
Дата
2021
Авторы
Kudryshov, N. A.
Moloshnikov, I. A.
Serenko, A. V.
Naumov, A. V.
Sboev, A. G.
Journal Title
Journal ISSN
Volume Title
Издатель
Аннотация
© 2021 Institute of Physics Publishing. All rights reserved.The large amount of data that has accumulated so far on the dynamics of the COVID-19 outbreak has allowed to assess the accuracy of forecasting methods in retrospect. This work is devoted to comparing a set of basic time series analysis methods for forecasting the number of confirmed cases for 14 days ahead: machine learning methods, exponential smoothing, autoregressive methods, along with variants of SIR and SEIR. On the year-long data for Moscow, the best basic model is showed to be SEIR within which the basic reproduction number R0 is predicted using a regression model, achieving the mean error of 16% by the MAPE metric. The resulting accuracy can be considered a baseline for a more complex prospective model that would be based on the presented approach.
Описание
Ключевые слова
Цитирование
Baseline accuracy of forecasting COVID-19 cases in Moscow region on a year in retrospect using basic statistical and machine learning methods / Kudryshov, N.A. [et al.] // Journal of Physics: Conference Series. - 2021. - 2036. - № 1. - 10.1088/1742-6596/2036/1/012029