Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries
- DOI
- 10.1007/s00477-022-02307-x
- Published
- 2022-10-05
- Container
- Stochastic Environmental Research and Risk Assessment
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s00477-022-02307-x,
title = {Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries},
author = {Iqra Sardar and Muhammad Azeem Akbar and Víctor Leiva and Ahmed Alsanad and Pradeep Mishra},
year = {2022},
journal = {Stochastic Environmental Research and Risk Assessment},
doi = {10.1007/s00477-022-02307-x},
url = {https://doi.org/10.1007/s00477-022-02307-x}
}RIS
TY - JOUR TI - Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries AU - Iqra Sardar AU - Muhammad Azeem Akbar AU - Víctor Leiva AU - Ahmed Alsanad AU - Pradeep Mishra PY - 2022 JO - Stochastic Environmental Research and Risk Assessment DO - 10.1007/s00477-022-02307-x UR - https://doi.org/10.1007/s00477-022-02307-x ER -
APA
Sardar, I., Akbar, M. A., Leiva, V., Alsanad, A., & Mishra, P. (2022). Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-022-02307-x
Source records
- crossref · retrieved 2026-09-26T12:30:58.308Z