Comparison of 80-20 Split and Walk-Forward Validation Techniques in Predicting COVID-19 Cases in Indonesia using the ARIMA Model.
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Abstract
This study presents a comparative analysis of the 80-20 split and walk-forward validation techniques for forecasting daily COVID-19 cases in Indonesia using the ARIMA model. Building on previous research, the ARIMA model has proven effective in various epidemiological contexts; however, this study highlights the critical importance of selecting the appropriate validation technique. The study uses data from January 3, 2020, to October 18, 2023, to develop a predictive model evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings indicate that the walk-forward validation technique outperforms the 80-20 split, with MAE of 137.32 and RMSE of 198.23, compared to the 80-20 split MAE of 4190.92 and RMSE of 4479.15. These results suggest that walk-forward validation provides more accurate and reliable predictions, particularly for dynamic and non-stationary data scenarios. This study underscores the significant impact of validation technique selection on ARIMA model performance, contributing new insights into forecasting methodologies in epidemiology.
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