Comparing the accuracy of several network-based COVID-19 prediction algorithms
Year of publication: |
2022
|
---|---|
Authors: | Achterberg, Massimo A. ; Prasse, Bastian ; Ma, Long ; Trajanovski, Stojan ; Kitsak, Maksim ; Mieghem, Piet van |
Published in: |
International journal of forecasting. - Amsterdam [u.a.] : Elsevier, ISSN 0169-2070, ZDB-ID 283943-X. - Vol. 38.2022, 2, p. 489-504
|
Subject: | Bayesian methods | Epidemiology | Forecast accuracy | Machine learning methods | Network inference | SIR model | Time series methods | Prognoseverfahren | Forecasting model | Künstliche Intelligenz | Artificial intelligence | Coronavirus | Zeitreihenanalyse | Time series analysis | Epidemie | Epidemic | Bayes-Statistik | Bayesian inference | Algorithmus | Algorithm |
-
From data to action : empowering COVID-19 monitoring and forecasting with intelligent algorithms
Charles, Vincent, (2024)
-
Kaggle forecasting competitions : an overlooked learning opportunity
Bojer, Casper Solheim, (2021)
-
Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemic
Caiado, Jorge, (2023)
- More ...
-
When Effort May Fail : Equilibria of Shared Effort with a Threshold
Polevoy, Gleb, (2022)
-
Minimizing the effective graph resistance by adding links is NP-hard
Kooij, Robert E., (2023)
-
The impact of the topology on cascading failures in a power grid model
Koç, Yakup, (2014)
- More ...