Title

Elastic Net to Forecast COVID-19 Cases

Publication Date

12-20-2020

Document Type

Conference Proceeding

Department

Computer Engineering; Applied Data Science

Publication Title

2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020

Conference Location

Sakheer, Bahrain

DOI

10.1109/3ICT51146.2020.9311968

Abstract

Forecasting novel daily cases of COVID-19 is crucial for medical, political, and other officials who handle day to day, COVID-19 related logistics. Current machine learning approaches, though robust in accuracy, can be either black boxes, specific to one region, and/or hard to apply if the user has nominal knowledge in machine learning and programing. This weakens the integrity of otherwise robust machine learning methods, causing them to not be utilized to their full potential. Thus, the presented Elastic Net COVID-19 Forecaster, or EN-CoF for short, is designed to provide an intuitive, generic, and easy to apply forecaster. EN-CoF is a multi-linear regressor trained on time series data to forecast number of novel daily COVID-19 cases. EN-CoF maintains a high accuracy on par with more complex models such as ARIMA and Bi-LSTM, while gaining the advantages of transparency, generalization, and accessibility.

Keywords

Artificial Intelligence, COVID-19, Elastic Net, Forecast, Machine Learning, Time Series

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