Forecasting assists governments, epidemiologists, and policymakers make calculated decisions to mitigate the spread of the COVID-19 pandemic, thus saving lives. This paper presents an ensemble machine learning model by combining the distinctive strengths of autoregressive integrated moving averages (ARIMA) and stacked long short-term memory networks (S-LSTM) using extensive training procedures and model integration algorithms. We validated the model's generalization capabilities by analyzing time series data of four countries, such as the Philippines, United States, India, and Brazil spanning 467 days. The quantitative results show that our ensemble model outperforms stand-alone models of ARIMA and S-LSTM for a 15-day forecast accuracy of 93.50% (infected cases) and 87.97% (death cases).
Paper
Full text
An Ensemble Machine Learning Approach For Time Series Forecasting of COVID-19 Cases
Semantic Scholar · Computer Science · 2021
Abstract
Forecasting assists governments, epidemiologists, and policymakers make calculated decisions to mitigate the spread of the COVID-19 pandemic, thus saving lives. This paper presents an ensemble machine learning model by combining the distinctive strengths of autoregressive integrated moving averages (ARIMA) and stacked long short-term memory networks (S-LSTM) using extensive training procedures and model integration algorithms. We validated the model's generalization capabilities by analyzing time series data of four countries, such as the Philippines, United States, India, and Brazil spanning 467 days. The quantitative results show that our ensemble model outperforms stand-alone models of ARIMA and S-LSTM for a 15-day forecast accuracy of 93.50% (infected cases) and 87.97% (death cases).