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Improving the performance of deep learning models using statistical features: The case study of COVID‐19 forecasting
- Source :
- Mathematical Methods in the Applied Sciences
- Publication Year :
- 2021
- Publisher :
- John Wiley and Sons Inc., 2021.
-
Abstract
- COVID-19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID-19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID-19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short-term memory model.
- Subjects :
- 2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19)
62‐07
General Mathematics
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Context (language use)
97r40
Machine learning
computer.software_genre
01 natural sciences
Convolutional neural network
Special Issue Paper
0101 mathematics
Combined method
Mathematics
Special Issue Papers
business.industry
Deep learning
010102 general mathematics
General Engineering
deep learning
COVID‐19 pandemic
010101 applied mathematics
hybrid methods
Memory model
Artificial intelligence
business
computer
statistical features
Subjects
Details
- Language :
- English
- ISSN :
- 10991476 and 01704214
- Database :
- OpenAIRE
- Journal :
- Mathematical Methods in the Applied Sciences
- Accession number :
- edsair.doi.dedup.....4f9cb4d1ad22c4ae5f16be9077f54a7c