Data Smoothing and Other Methods for Generating Forecasts for COVID-19 Cases in Sri Lanka

Abstract

The COVID-19 pandemic has significantly impacted global society, including Sri Lanka, necessitating the need for reliable forecasting methods. This study compares ten distinct models to predict the number of confirmed COVID-19 cases in Sri Lanka, aiming to assess the performance of statistical models using limited and volatile realworld data characterized by trends, random peaks, and autocorrelations. In addition to the classical ARIMA model, various smoothing and filtering techniques were explored to capture the unique characteristics of the data. The model consistencies in multiple-day predictions were demonstrated, and robust evaluation criteria, along with non-robust measures, were utilized to enhance the effectiveness of the evaluation process. The results highlight the effectiveness of traditional smoothing strategies such as Simple Exponential Smoothing, Holt’s Exponential Smoothing, and the Smoothing Splines technique coupled with the ARIMA model. Notably, applying the ARIMA model directly to the original data without smoothing or filtering approaches yielded inadequate forecasts, underscoring its limitations in volatile data settings.