MAZUREK, Jiří. The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent. PLOS ONE. roč. 16, č. 5, s. 1-9. ISSN 1932-6203. doi:10.1371/journal.pone.0252394. 2021.
Další formáty:   BibTeX LaTeX RIS
Základní údaje
Originální název The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent
Autoři MAZUREK, Jiří (203 Česká republika, garant, domácí).
Vydání PLOS ONE, 2021, 1932-6203.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 30303 Infectious Diseases
Stát vydavatele Spojené státy
Utajení není předmětem státního či obchodního tajemství
WWW URL
Kód RIV RIV/47813059:19520/21:A0000244
Organizační jednotka Obchodně podnikatelská fakulta v Karviné
Doi http://dx.doi.org/10.1371/journal.pone.0252394
UT WoS 000664636100043
Klíčová slova anglicky prediction; COVID-19; Lyapunov exponent; chaotic system
Štítky impakt
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Miroslava Snopková, učo 43819. Změněno: 12. 4. 2022 10:13.
Anotace
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.
VytisknoutZobrazeno: 29. 3. 2024 08:08