Semantic Scholar Open Access 2020 446 sitasi

Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study

Abdelhafid Zeroual F. Harrou Abdelkader Dairi Ying Sun

Lihat Sumber DOI

Abstrak

Highlights • Developed deep learning methods to forecast the COVID19 spread.• Five deep learning models have been compared for COVID-19 forecasting.• Time-series COVID19 data from Italy, Spain, France, China, the USA, and Australia are used.• Results demonstrate the potential of deep learning models to forecast COVID19 data.• Results show the superior performance of the Variational AutoEncoder model.

Topik & Kata Kunci

Medicine

Penulis (4)

Abdelhafid Zeroual

F. Harrou

Abdelkader Dairi

Ying Sun

Format Sitasi

APA MLA BibTeX

Zeroual, A., Harrou, F., Dairi, A., Sun, Y. (2020). Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. https://doi.org/10.1016/j.chaos.2020.110121

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.chaos.2020.110121

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 446×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.chaos.2020.110121
Akses: Open Access ✓