Forecasting COVID-19 Number of Cases by Implementing ARIMA and SARIMA with Grid Search in the United States
Abolmaali, S.; Shirzaei, S.
Show abstract
COVID-19 has surged in the United States since January 2020. Since then, social distancing and lockdown have helped many people to avoid infectious diseases. However, this did not help the upswing of the number of cases after the lockdown was finished. Modeling the infectious disease can help the health care providers and governors to plan ahead for obtain the needed resources. In this manner, precise short-term determining of the number of cases can be imperative to the healthcare system. Many models have been used since the pandemic has started. In this paper we will compare couple of time series models like Simple Moving Average, Exponentially Weighted Moving Average, Holt-Winters Double Exponential Smoothing Additive, ARIMA, and SARIMA. Two models that have been used to predict the number of cases are ARIMA and SARIMA. A grid search has been implemented to select the best combination of the parameters for both models. Results show that in the case of modeling, the Holt-Winters Double Exponential model outperforms Exponentially Weighted Moving Average and Simple Moving Average while forecasting ARIMA outperforms SARIMA.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Projections and fractional dynamics of COVID-19 with optimal control analysis 94%
- Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators 94%
- Simulation of foraging behavior using a decision-making agent with Bayesian and inverse Bayesian inference: Temporal correlations and power laws in displacement patterns 93%
Similar papers in this journal
Similar papers in this journal
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 96%
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 96%
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.