Missing values substantially degrade the reliability of environmental time-series forecasting; however, prior studies largely evaluate imputation methods in isolation without systematically linking missingness mechanisms to deep learning forecasting performance. To address this gap, this study proposes a mechanism-aware comparative framework that evaluates deletion and six imputation methods (Mean, Median, Mode, LOCF, KNN, and MICE) across three environmental time-series datasets with naturally occurring missing values, using LSTM as the forecasting model. The novelty lies in jointly analyzing statistical error (MAPE, RMSE), goodness-of-fit (R²), and statistical significance to identify structurally aligned imputation strategies under different missingness patterns. Experimental results show that deletion as baseline consistently produces the worst performance (MAPE: 5.91429; 7.35000; 2.84881), whereas imputation reduces proportional error by more than 70% on average (p < 0.05). LOCF performs best under temporal dependency (MAPE 0.73959; R² 0.92757), KNN achieves the most balanced performance under MCAR-like behavior (R² 0.94086), and Mean imputation yields the lowest error in MAR-structured data (MAPE 0.41560; R² 0.97077). These findings demonstrate that imputation effectiveness depends on alignment with missingness structure rather than methodological complexity, providing evidence-based guidance for robust environmental.
D. Thifal, Aji Prasetya Wibawa, Adelia Desyana Eka Putri et al.· Jurnal Informatika· 0 citations
Time series data in climatology and environmental research are highly susceptible to missing values that can disrupt temporal structures and degrade forecasting performance. This study evaluates the effectiveness of several imputation methods in improving the predictive performance of a Bidirectional Long Short-Term Memory model across three missing-data mechanisms: Missing Completely at Random, Missing at Random, and Missing Not at Random. The compared methods include mean, median, mode, k-nearest neighbors, multiple imputation by chained equations, and last observation carried forward, with data deletion serving as the baseline. All datasets were normalized using the min–max technique, and model hyperparameters were optimized through Particle Swarm Optimization. Performance was assessed using mean absolute percentage error, root mean square error, and the coefficient of determination. The findings indicate that proper imputation significantly enhances forecasting accuracy compared to deleting incomplete observations. In Dataset 1, the last observation carried forward achieved the best performance with a coefficient of determination of 0.923 and a root mean square error of 3.373. Similarly, Dataset 2 showed optimal results with the same method, producing a coefficient of determination of 0.950 and a root mean square error of 14.458. The most substantial improvement was observed in Dataset 3, where mean imputation reduced the mean absolute percentage error from 3.219 to 0.329 while increasing the coefficient of determination to 0.986. These results highlight the critical role of selecting an imputation strategy in deep learning-based time series forecasting and provide practical guidance for handling incomplete environmental datasets.
