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

• URL: http://arxiv.org/abs/2605.24055v1
• Date: Fri, 22 May 2026 00:59:56 GMT
• Title: Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions
• Abstract summary: Real-world time-series data in industrial sensing, healthcare, and energy systems is often corrupted by a mixture of Gaussian noise.<n>We propose Cascade-KDE, a training-free restoration framework for corrupted time series.
• Score: 3.4633810891614267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Rethinking Reservoir Pruning: A Dynamical Perspective for Echo State Networks [0.0]
  We propose Dynamical Mode Pruning (DMP), a reservoir pruning method that ranks neurons by their contribution to dominant transition modes.<n>DMP improves or preserves forecasting accuracy while reducing redundant reservoir components.
  arXiv  Detail & Related papers  (2026-08-05T08:56:17Z)
• Lindblad-Inspired Multi-Timescale Reservoir Computing with Separable Rotation and Dissipation [0.08057006406834467]
  We introduce a classical Lindblad-inspired multi-timescale reservoir that bridges open-system dynamical principles with structured state-space modeling.<n>We evaluate the method over ten aligned seeds against standard, leaky, deep, gated, cycle, and next-generation reservoirs.<n>Across the benchmark suite, the proposed reservoir achieves the best fixed-reservoir performance on bounded NARMA-20 and the lowest mean error on Lorenz-63.
  arXiv  Detail & Related papers  (2026-07-27T18:48:32Z)
• Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits [3.2848713528308817]
  We study non-stationary linear contextual bandits where the reward model drifts over time.<n>We propose Flow-Corrected Thompson Sampling (fcTS), a Bayesian method that reuses experience by transporting past rewards to the present.
  arXiv  Detail & Related papers  (2026-06-22T20:52:19Z)
• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals [83.0127582612634]
  Near-boundary Rescue (NSR) is a minimal, plug-and-play modification that retains out-of-bound tokens to recover lost signals.<n>NSR substantially improves training stability and delivers consistent gains over strong baselines such as DAPO and GSPO.
  arXiv  Detail & Related papers  (2026-05-21T16:45:31Z)
• CAdam: Context-Adaptive Moment Estimation for 3D Gaussian Densification in Generative Distillation [3.4998703934432682]
  We introduce Context-Adaptive Moment Estimation (CAdam) to solve the Densification Dilemma.<n>CAdam reduces Gaussian count by 85%-97% relative to standard densification while preserving overall comparable quality.<n>These results highlight signal-aware density control as a practical way to improve memory efficiency in optimization-based generative distillation.
  arXiv  Detail & Related papers  (2026-05-20T08:08:39Z)
• Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation [0.0]
  Unmanned Aerial Vehicles in dynamic environments face telemetry outages, structural vibrations, and regime-dependent noise.<n>We introduce the N-Deep Recurrent Sage-Husa Filter (NDR-SHKF), which replaces the scalar parameter with a vector-valued memory attenuation policy.<n>A bifurcated architecture routes shallow recurrent states to capture instantaneous sensor anomalies and deep states to encode sustained dynamic trends.
  arXiv  Detail & Related papers  (2026-05-18T17:38:13Z)
• Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency [12.266189649117003]
  We replace local supervision with a global structural constraint.<n>We train a time-conditioned secant velocity field whose deviation from this property serves two purposes.<n>As a training regularizer, it confines the hypothesis space to flows that compose consistently across temporal scales.
  arXiv  Detail & Related papers  (2026-05-08T20:24:32Z)
• Physics-Informed Temporal U-Net for High-Fidelity Fluid Interpolation [0.0]
  We propose a novel Temporal U-Net architecture that integrates a VGG-based perceptual loss along with a Physics-Informed Bridge to overcome these issues.<n> Experimental results on multi-channel RGB fluid data show that our method clearly outperforms standard models.
  arXiv  Detail & Related papers  (2026-04-25T16:36:52Z)
• Unbiased Gradient Estimation for Event Binning via Functional Backpropagation [64.88399635309918]
  We propose a novel framework for unbiased gradient estimation of arbitrary binning functions by synthesizing weak derivatives during backpropagation.<n>We achieve 9.4% lower EPE in self-supervised optical flow, and 5.1% lower RMS error in SLAM, demonstrating broad benefits for event-based visual perception.
  arXiv  Detail & Related papers  (2026-02-13T04:05:03Z)
• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting [79.37674445572462]
  Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends.<n>Treating reshaped tensors as static images results in a topological mismatch.<n> relying on uniform fixed-size representations allocates modeling capacity inefficiently.<n>TimeGS is a novel framework that fundamentally shifts the forecasting paradigm from regression to 2D generative rendering.
  arXiv  Detail & Related papers  (2026-02-10T14:13:36Z)
• The Procrustean Bed of Time Series: The Optimization Bias of Point-wise Loss [53.542743390809356]
  This paper aims to provide a first-principles analysis of the Expectation of Optimization Bias (EOB)<n>Our analysis reveals a fundamental paradigm paradox: the more deterministic and structured the time series, the more severe the bias by point-wise loss function.<n>We present a concrete solution that simultaneously achieves both principles via DFT or DWT.
  arXiv  Detail & Related papers  (2025-12-21T06:08:22Z)
• Event Signal Filtering via Probability Flux Estimation [58.31652473933809]
  Events offer a novel paradigm for capturing scene dynamics via asynchronous sensing, but their inherent randomness often leads to degraded signal quality.<n>Event signal filtering is thus essential for enhancing fidelity by reducing this internal randomness and ensuring consistent outputs across diverse acquisition conditions.<n>This paper introduces a generative, online filtering framework called Event Density Flow Filter (EDFilter)<n>Experiments validate EDFilter's performance across tasks like event filtering, super-resolution, and direct event-based blob tracking.
  arXiv  Detail & Related papers  (2025-04-10T07:03:08Z)
• HADL Framework for Noise Resilient Long-Term Time Series Forecasting [0.7810572107832383]
  Long-term time series forecasting is critical in domains such as finance, economics, and energy.<n>The impact of temporal noise in extended lookback windows remains underexplored, often degrading model performance and computational efficiency.<n>We propose a novel framework that addresses these challenges by integrating the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT)<n>Our approach demonstrates competitive robustness to noisy input, significantly reduces computational complexity, and achieves competitive or state-of-the-art forecasting performance across diverse benchmark datasets.
  arXiv  Detail & Related papers  (2025-02-14T21:41:42Z)
• Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective [66.80315289020487]
  Warmup-Stable-Decay (WSD) schedule uses a constant learning rate to produce a main branch of iterates that can continue indefinitely without a pre-specified compute budget.<n>We show that pretraining loss exhibits a river valley landscape, which resembles a deep valley with a river at its bottom.<n>Inspired by the theory, we introduce WSD-S, a variant of WSD that reuses previous checkpoints' decay phases and keeps only one main branch.
  arXiv  Detail & Related papers  (2024-10-07T16:49:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.