A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

Abstract

The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

Моделі Глибокого Навчання Для Прогнозування Часових Рядів

“…У [5] проведено порівняльний аналіз прогнозування часових рядів на основі моделі BiLSTM та статистичними методами, для вивчення розвитку та поширення пандемії COVID-19 в Саудівській Аравіїи. Відповідно до результатів дослідження, запропонована модель BiLSTM показала значно вищу ефективність відносно використання статистичних методів прогнозування часових рядів.…”

Hybrid Prediction Model Based on Decomposed and Synthesized COVID-19 Cumulative Confirmed Data

“…Among these models, Encoding-Decoding-LSTM achieved the best prediction effect. In [14], BILSTM-Encoding-Decoding and 16 other models were used to make comparative predictions based on the epidemic data from Saudi Arabia. In the end, BILSTM-Encoding-Decoding achieved the best results, but a problem was also raised in [14], in that it failed to consider the impact of relevant policies on the development of the epidemic.…”

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

“…Simultaneously, the other backward LSTM moves future data information from the output sequence to the input sequence. This mechanism enables the model to learn long-term dependencies and enhance its accuracy [40]. Bi-LSTM architecture is given in Figure 2.…”