When Do We Need Complex Generative Models for Time Series Imputation? A Case Study on Battery and Electrical Degradation Data

Missing-value imputation is critical for industrial monitoring and sensor-to-RUL pipelines in battery and electrical systems. Diffusion models perform well on complex time series, but their necessity for univariate, smooth, small-sample electrical degradation signals remain unclear. We evaluated DDI-E (a conditional diffusion imputer) against linear interpolation (LI) and K-nearest neighbors (KNN) on NASA battery capacity and NASA IGBT leakage-current datasets under 10–90% random missingness, with leave-one-out cross-validation on the battery data. LI/KNN achieved practically sufficient accuracy (battery MAE: 0.007–0.020 Ah), whereas DDI-E did not improve performance (battery MAE: 0.39–0.43 Ah, about 20–58× LI; IGBT MAPE: LI/KNN near 0% vs. DDI-E about 18%). These results indicate an applicability boundary: for univariate, smooth, small-sample electrical degradation data, traditional interpolation is often sufficient, while the extra complexity of diffusion modeling may not yield additional benefit. Combined with our previous positive results on complex multi-channel data, we provide a data-characteristic-driven framework for imputation-method selection and practical guidance for industrial sensor-to-RUL workflows.