APPLICATION OF ARIMA MODELS FOR FORECASTING COVID-19 IN AZERBAIJAN - Problems of Information Technology

APPLICATION OF ARIMA MODELS FOR FORECASTING COVID-19 IN AZERBAIJAN - Problems of Information Technology

APPLICATION OF ARIMA MODELS FOR FORECASTING COVID-19 IN AZERBAIJAN - Problems of Information Technology

APPLICATION OF ARIMA MODELS FOR FORECASTING COVID-19 IN AZERBAIJAN - Problems of Information Technology

APPLICATION OF ARIMA MODELS FOR FORECASTING COVID-19 IN AZERBAIJAN - Problems of Information Technology
APPLICATION OF ARIMA MODELS FOR FORECASTING COVID-19 IN AZERBAIJAN - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№1, 2021

APPLICATION OF ARIMA MODELS FOR FORECASTING COVID-19 IN AZERBAIJAN

Firangiz I. Sadiyeva

The article proposes an Autoregressive Integrated Moving Average (ARIMA) model to predict the COVID-19 pandemic. COVID-19 is a new type of pandemic that is spreading rapidly around the world and is currently continuing.  Recently, the number of pandemic cases in Azerbaijan has reached the highest rate. For this reason, the forecast of the COVID-19 pandemic is reviewed, and the COVID-19 time series of the ARIMA model proposed in experiments with real data covering several months is used with different parameters for forecasting. According to the data, the number of daily infections officially registered by the Ministry of Healthcare of the Republic of Azerbaijan (www.sehiyye.gov.az) between 22.01.2020 - 22.10.2020 is considered. Using these data, the incidence of infections in our country in the next period is forecasted. For this purpose, various parameters are given to the ARIMA model and the error rate of each model was evaluated accordingly. MAPE (Mean Absolute Percentage Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) functions are used to estimate the error. As a result of comparisons, the most suitable model is selected. The results obtained are an important factor for both the health care system and ordinary citizens during the pandemic in our country. The results confirm that statistical methods can be effective in applying non-stationary coronavirus time-series predictions to other issues (pp.95–104)

Keywords: COVID-19, coronavirus, ARIMA, forecast, time-series.
DOI : 10.25045/jpit.v12.i1.08
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