Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions
For practitioners in industrial sensing, healthcare, and energy systems who need robust preprocessing that preserves local shape features, this method offers a practical, training-free alternative to existing approaches.
Cascade-KDE is a training-free framework for restoring time-series data corrupted by Gaussian noise and impulse outliers, achieving consistent gains over classical filters and learning-based baselines in curve fidelity, derivative preservation, downstream classification, and runtime efficiency across several benchmark datasets.
Real-world time-series data in industrial sensing, healthcare, and energy systems is often corrupted by a mixture of Gaussian noise and occasional large-magnitude impulse outliers. For tasks that depend on local shape, such as ECG morphology analysis and battery degradation monitoring, the main requirement is not only low reconstruction error but also preservation of derivative peaks and task-critical features. We propose Cascade-KDE, a training-free restoration framework for corrupted time series. The method first estimates a two-dimensional temporal-amplitude density, then applies a Density-Truncated Robust Expectation to limit the influence of distant abnormal points, and finally refines the sequence through an exponential cascade with adaptive stopping. This design aims to improve robustness under out-of-distribution impulse corruptions while keeping the restored trajectory close to the original local structure. Across several benchmark datasets, the proposed method shows consistent gains over classical filters and representative learning-based baselines on curve fidelity, derivative preservation, downstream classification, and runtime efficiency. These results suggest that bounded density-based restoration is a practical option for feature-preserving preprocessing in noisy time-series pipelines.