Using exponential smoothing methods to analysis COVID-19 time series

• Main Authors: Abidi, Fadhil A., Radiy, Zainb Hasan
• Format: Conference Proceeding
• Language: English
• Subjects: COVID-19; Design of experiments; Mathematical models; Outliers (statistics); Parameter robustness; Robustness (mathematics); Smoothing; Statistical analysis; Statistical methods; Time series
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0200417

Exponential smoothing models are used to smooth time series data and treat the impact of Trend or Seasonal in the time series. There are several models including of smoothing: Simple Exponential Smoothing, Double Smoothing and Holt-Winters smoothing. Robust statistical methods in time series models or in linear models (Regression, Experiment Design) they are a suitable to treat the problem of outlier values in data in order to obtain more reliable forecast results. In this paper, we applied the Robust Exponential smoothing method Compared with some anther exponential as :(Double smoothing, Holt-Winters Additive/Multiplicative smoothing, EST method) for estimation model parameters and forecasting are presented. and application of all these methods in Data of corona Pandemic (COVID-19) from Iraq in period (Mar. 2020 – Mar 2022), the robust exponential method is presented that applies the standard technique to preclean data from the outlier values in time series.We depended on R-packages functions as [ses(),EST, robiest(), hw(),…],it use to obtain smoothing parameters are robust. In this research we found Robust method have a good forecast performance for time series with an outlier values.