A SEASONAL IMPUTATION METHOD FOR ADDRESSING MISSING DATA IN ENVIRONMENTAL IOT SENSOR TIME SERIES - Ardiansyah Ramadhan; Surya Micrandi Nasution; Reza Rendian Septiawan; I Kadek Nuary Trisnawan; Angel Metanosa Afinda (Kresnamedia Publisher, 2026)
Missing and incomplete observations in Environmental IoT sensor networks reduce data reliability and disrupt analyses, especially for temperature and humidity time series exhibiting strong diurnal seasonality. This study develops and evaluates a seasonal imputation method to address missing data in IoT-based environmental monitoring, using a workflow of anomaly detection, outlier removal, time-of-day-aware imputation, and performance evaluation under varying missing-rate scenarios. Key challenges include sensor noise, connectivity issues, and intermittent hardware failures, which degrade data integrity and affect trend analysis, forecasting, and anomaly detection. To mitigate these, the method uses hourly and minute-level seasonal patterns after filtering out physically unrealistic values. Experimental results show high accuracy and robustness in reconstructing temperature and humidity data: temperature imputation achieves MAE values of approximately 0.86–0.87°C, and humidity yields MAE values of 3.92–4.01%RH, with no performance drop even at 50% data loss. The imputed series preserves natural diurnal dynamics without introducing distortions, effectively restoring continuity and structural consistency in environmental IoT time series for reliable modeling, feature extraction, and decision support.