Smoothing Techniques for Improving COVID-19 Time Series Forecasting Across Countries
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computation-13-00136-v2.pdf
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21.03 MB
Format
Adobe PDF
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5ad74ea8388e0440de51e7e79a803902
Author(s)
Zbezhkhovska, Uliana
Chumachenko, Dmytro
Date Issued
June 3, 2025
Journal
Computation
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Zbezhkhovska, U.; Chumachenko, D. Smoothing Techniques for Improving COVID-19 Time Series Forecasting Across Countries. Computation 2025, 13, 136.
Version
Final published version
Abstract
Accurate forecasting of COVID-19 case numbers is critical for timely and effective public health interventions. However, epidemiological data’s irregular and noisy nature often undermines the predictive performance. This study examines the influence of four smoothing techniques—the rolling mean, the exponentially weighted moving average, a Kalman filter, and seasonal–trend decomposition using Loess (STL)—on the forecasting accuracy of four models: LSTM, the Temporal Fusion Transformer (TFT), XGBoost, and LightGBM. Weekly case data from Ukraine, Bulgaria, Slovenia, and Greece were used to assess the models’ performance over short- (3-month) and medium-term (6-month) horizons. The results demonstrate that smoothing enhanced the models’ stability, particularly for neural architectures, and the model selection emerged as the primary driver of predictive accuracy. The LSTM and TFT models, when paired with STL or the rolling mean, outperformed the others in their short-term forecasts, while XGBoost exhibited greater robustness over longer horizons in selected countries. An ANOVA confirmed the statistically significant influence of the model type on the MAPE (p = 0.008), whereas the smoothing method alone showed no significant effect. These findings offer practical guidance for designing context-specific forecasting pipelines adapted to epidemic dynamics and variations in data quality.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/computation13060136
