Time-Series Forecasting of COVID-19 Cases Using Stacked Long Short-Term Memory Networks

The extent of the COVID-19 pandemic has devastated world economies and claimed millions of lives. Timely and accurate information such as time-series forecasting is crucial for government, healthcare systems, decision-makers, and policy-implementers in managing the disease's progression. With the potential value of early knowledge to save countless lives, the research investigated and compared the capabilities and robustness of sophisticated deep learning models to traditional time-series forecasting methods. The results show that the Stacked Long Short-Term Memory Networks (SLSTM) outperforms the Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA) models for a 15-day forecast horizon. SLSTM attained a collective mean accuracy of 92.17% (confirmed cases) and 82.31% (death cases) using historical data of 419 days from March 6, 2020 to April 28, 2021 of four countries - the Philippines, United States, India, and Brazil.