COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data
A compounded method—exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques—is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the COVID-19 virus in Italy. Fu...
Saved in:
Main Authors: | Livio Fenga, Carlo Del Castello |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | Journal of Probability and Statistics |
Online Access: | http://dx.doi.org/10.1155/2021/1235973 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Forecasting the COVID-19 Diffusion in Italy and the Related Occupancy of Intensive Care Units
by: Livio Fenga
Published: (2021-01-01) -
Bootstrap Order Determination for ARMA Models: A Comparison between Different Model Selection Criteria
by: Livio Fenga
Published: (2017-01-01) -
Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data
by: Rashad M. EL-Sagheer, et al.
Published: (2022-01-01) -
Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques
by: Peng He, et al.
Published: (2024-12-01) -
Bootstrap methods in selection of the discriminant subspace
by: Gintautas Jakimauskas, et al.
Published: (2000-12-01)