Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET
| Autor: | Bellatasya Unrica Nadia, William Darmawan, Christophorus Beneditto Aditya Satrio, Novita Hanafiah |
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| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19) Computer science Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) education 020206 networking & telecommunications social sciences 02 engineering and technology Pandemic 0202 electrical engineering electronic engineering information engineering Econometrics General Earth and Planetary Sciences 020201 artificial intelligence & image processing Autoregressive integrated moving average Time series health care economics and organizations General Environmental Science |
| Zdroj: | Procedia Computer Science. 179:524-532 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2021.01.036 |
| Popis: | The spread of COVID-19 has caused it to be a pandemic. This has caused massive disruption to our daily lives, both directly and indirectly. We aim to utilize Machine Learning model in attempt to forecast the trend of the disease in Indonesia with finding out the approximation when normality will return. This study uses Facebook’s Prophet Forecasting Model and ARIMA Forecasting Model to compare their performance and accuracy on dataset containing the confirmed cases, deaths, and recovered numbers, obtained from the Kaggle website. The forecast models are then compared to the last 2 weeks of the actual data to measure their performance against each other. The result shows that Prophet generally outperforms ARIMA, despite it being further from the actual data the more days it forecasts. |
| Databáze: | OpenAIRE |
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