Abstract
COVID-19 has sparked a worldwide pandemic, with the number of infected cases and deaths rising on a regular basis. Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. Thus, if we can accurately forecast the trend of cases globally, the spread of the pandemic can be controlled. In this study, a hybrid CNN-LSTM model was developed on a time-series dataset to forecast the number of confirmed cases of COVID-19. The proposed model was evaluated and compared with 17 baseline models on test and forecast data. The primary finding of this research is that the proposed CNN-LSTM model outperformed them all, with the lowest average MAPE, RMSE, and RRMSE values on both test and forecast data. Conclusively, our experimental results show that, while standalone CNN and LSTM models provide acceptable and efficient forecasting performance for the confirmed COVID-19 cases time series, combining both models in the proposed CNN-LSTM encoder-decoder structure provides a significant boost in forecasting performance. Furthermore, we demonstrated that the suggested model produced satisfactory predicting results even with a small amount of data.
Highlights
Introduction e year2020 witnessed the global spread of the coronavirus disease (COVID-19) pandemic [1]
RQ1 Answer: e Performance of the Proposed Model Compared to the Baseline Models on the Test Data. e predicted and actual values between July 18, 2020 and August 14, 2020 were plotted in 5 different graphs according to the type of the models. e values predicted by the proposed model were plotted in each graph to compare the trend obtained with other baseline models
Similar to the processes that were conducted on the test data, the forecasted and actual values between September 12, 2020 and September 18, 2020 were plotted in 5 different graphs according to the type of the models. e values forecasted by Actual Predicted by convolutional neural networks (CNNs)-long short-term memory (LSTM) Predicted by CNN-1D Predicted by LSTM
Summary
Time-series prediction is a forecasting method that analyses historical data to capture the relationship and trends of a random variable. As one of the most successful deep learning methods, LSTM has been utilized to predict COVID-19 cases in many researches [14, 17, 18, 21, 25]. Convolutional neural network (CNN) is another well-known deep learning and has been applied in forecasting COVID-19 cases [40,41,42]. Results from these studies showed that CNN is excellent for filtering out noise in input data and extracting more beneficial features for the final forecasting model. A time-series forecasting model that employs both deep learning methods, namely, LSTM and CNN, may improve prediction accuracy.
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