 Comparison of Time Series Regression, Support Vector Regression, Hybrid, and Ensemble Method to Forecast PM2.5Elly Pusporani, Ghisella Asy Sifa, Nurin Faizun, Pressylia Aluisina Putri Widyangga, Adma Novita Sari, M. Fariz Fadillah Mardianto and Sediono Sediono Data and Metadata, 2025, vol. 4, 862 Abstract: Introduction: PM2.5 pollution poses significant health risks, particularly in Jakarta, where levels often exceed safety thresholds. Accurate forecasting models are essential for air quality management and mitigation strategies. Methods: This study compares four forecasting models: Time Series Regression (TSR), Support Vector Regression (SVR), a hybrid TSR-SVR model, and an ensemble approach. The dataset consists of 9,119 hourly PM2.5 observations from January 1, 2023, to January 15, 2024. Missing values were imputed using historical hourly trends. Model performance was evaluated using Root Mean Squared Error (RMSE). Results: The hybrid TSR-SVR model achieved the lowest RMSE (6.829), outperforming TSR (7.595), SVR (7.477), and the ensemble method (7.486). The hybrid approach effectively captures both linear and nonlinear patterns in PM2.5 fluctuations, making it the most accurate model. Conclusions: Integrating statistical and machine learning models improves PM2.5 forecasting accuracy, aiding policymakers in pollution control efforts. Future studies should explore additional external factors to enhance prediction performance. Date: 2025 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:862:id:1056294dm2025862 DOI: 10.56294/dm2025862 Access Statistics for this article More articles in Data and Metadata from AG Editor |
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