Recently, researchers have applied the Artificial Intelligence, especially deep learning in forecasting, instead of the traditional econometrics’ methods. They argue that deep learning approach can improve the forecasting results by reducing the forecasting errors and saving time and cost. However, there is no empirical evidence for that, since there is a lack of research comparing the forecasting performance of these approaches. Consequently, the aim of this paper is to examine the latter argument and to identify the best technique to be used in forecasting the Covid-19 pandemic and others. In fact, the Covid-19 pandemic is defining a global health crisis, which is the hugest the world has faced since World War II. In addition to being threatened by GDP decline and income losses; fears of fetal effects of this epidemic makes it critical to predict the potential spread and identify the best techniques to be applied for that purpose. To achieve the aim of this study, two different methods of forecasting, namely an econometric approach named Autoregressive-Distributed Lag (ARDL) and a deep learning model named Long Short-Term Memory (LSTM) are utilized to forecast the number of daily cases and deaths of Covid-19 in Egypt (March 2020 - March 2021). Consequently, the contribution of this paper is twofold; first, investigating the best way of forecasting the Covid-19 epidemic especially, and therefore real-life phenomena in general, second, assessing the impact of mobility on the incidence of the pandemic in Egypt. The results revealed that the LSTM method shows a slightly better forecasting performance even without using mobility data.
Nossir, S., Al-Shobaki, S., & Salah Bayram, R. (2022). Comparing Econometrics Approach Vs. Deep Learning Approach in Forecasting Covid-19 Infections and Deaths Horizon in Egypt. Scientific journal of the Faculty of Economic Studies and Political Science, 7(14), 11-52. doi: 10.21608/esalexu.2022.247220
MLA
Shereen Nossir; Sarah Al-Shobaki; Reham Salah Bayram. "Comparing Econometrics Approach Vs. Deep Learning Approach in Forecasting Covid-19 Infections and Deaths Horizon in Egypt", Scientific journal of the Faculty of Economic Studies and Political Science, 7, 14, 2022, 11-52. doi: 10.21608/esalexu.2022.247220
HARVARD
Nossir, S., Al-Shobaki, S., Salah Bayram, R. (2022). 'Comparing Econometrics Approach Vs. Deep Learning Approach in Forecasting Covid-19 Infections and Deaths Horizon in Egypt', Scientific journal of the Faculty of Economic Studies and Political Science, 7(14), pp. 11-52. doi: 10.21608/esalexu.2022.247220
VANCOUVER
Nossir, S., Al-Shobaki, S., Salah Bayram, R. Comparing Econometrics Approach Vs. Deep Learning Approach in Forecasting Covid-19 Infections and Deaths Horizon in Egypt. Scientific journal of the Faculty of Economic Studies and Political Science, 2022; 7(14): 11-52. doi: 10.21608/esalexu.2022.247220
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