MAZUREK, Jiří. The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent. PLOS ONE. vol. 16, No 5, p. 1-9. ISSN 1932-6203. doi:10.1371/journal.pone.0252394. 2021.
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Basic information
Original name The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent
Authors MAZUREK, Jiří (203 Czech Republic, guarantor, belonging to the institution).
Edition PLOS ONE, 2021, 1932-6203.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 30303 Infectious Diseases
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/47813059:19520/21:A0000244
Organization unit School of Business Administration in Karvina
Doi http://dx.doi.org/10.1371/journal.pone.0252394
UT WoS 000664636100043
Keywords in English prediction; COVID-19; Lyapunov exponent; chaotic system
Tags impakt
Tags International impact, Reviewed
Changed by Changed by: Miroslava Snopková, učo 43819. Changed: 12/4/2022 10:13.
Abstract
In the field of machine learning, building models and measuring their performance are two equally important tasks. Currently, measures of precision of regression models’ predictions are usually based on the notion of mean error, where by error we mean a deviation of a prediction from an observation. However, these mean based measures of models’ performance have two drawbacks. Firstly, they ignore the length of the prediction, which is crucial when dealing with chaotic systems, where a small deviation at the beginning grows exponentially with time. Secondly, these measures are not suitable in situations where a prediction is made for a specific point in time (e.g. a date), since they average all errors from the start of the prediction to its end. Therefore, the aim of this paper is to propose a new measure of models’ prediction precision, a divergence exponent, based on the notion of the Lyapunov exponent which overcomes the aforementioned drawbacks. The proposed approach enables the measuring and comparison of models’ prediction precision for time series with unequal length and a given target date in the framework of chaotic phenomena. Application of the divergence exponent to the evaluation of models’ accuracy is demonstrated by two examples and then a set of selected predictions of COVID-19 spread from other studies is evaluated to show its potential.
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