Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions

ArXi:2605.24055v1 Announce Type: cross 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