  Coronavirus disease 2019 (COVID-19) is a global public health crisis that has
been declared a pandemic by World Health Organization. Forecasting country-wise
COVID-19 cases is necessary to help policymakers and healthcare providers
prepare for the future. This study explores the performance of several Long
Short-Term Memory (LSTM) models and Auto-Regressive Integrated Moving Average
(ARIMA) model in forecasting the number of confirmed COVID-19 cases. Time
series of daily cumulative COVID-19 cases were used for generating 1-day,
3-day, and 5-day forecasts using several LSTM models and ARIMA. Two novel
k-period performance metrics - k-day Mean Absolute Percentage Error (kMAPE) and
k-day Median Symmetric Accuracy (kMdSA) - were developed for evaluating the
performance of the models in forecasting time series values for multiple days.
Errors in prediction using kMAPE and kMdSA for LSTM models were both as low as
0.05%, while those for ARIMA were 0.07% and 0.06% respectively. LSTM models
slightly underestimated while ARIMA slightly overestimated the numbers in the
forecasts. The performance of LSTM models is comparable to ARIMA in forecasting
COVID-19 cases. While ARIMA requires longer sequences, LSTMs can perform
reasonably well with sequence sizes as small as 3. However, LSTMs require a
large number of training samples. Further, the development of k-period
performance metrics proposed is likely to be useful for performance evaluation
of time series models in predicting multiple periods. Based on the k-period
performance metrics proposed, both LSTMs and ARIMA are useful for time series
analysis and forecasting for COVID-19.
