arXiv Machine Learning
Benchmarking Sensor-Fault Robustness in Forecasting
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Sep 17
Reliable Virtual Sensing: A Multi-Domain Benchmark for Robustness Under Sensor Failures
The paper introduces MuViS-C, a multi‑domain benchmark that evaluates the robustness of learning‑based virtual sensing models against ten common sensor failure modes, ranging from subtle drifts to catastrophic dropouts. It assesses models using average error, relative degradation, and worst‑case fragility across nine datasets from six domains, comparing six architectures (gradient‑boosted trees, convolution, recurrence, attention, and MLP‑mixing). The study finds that all models degrade under corruption, gradient‑boosted trees are most robust, and targeted robustification can improve attention models at the cost of nominal performance.
Hugging Face Trending Papers
Sep 16
Reliable Virtual Sensing: A Multi-Domain Benchmark for Robustness Under Sensor Failures
Virtual sensing, the estimation of hard-to-measure quantities from available sensor measurements, is a critical enabler for control and monitoring in cyber-physical systems. However, when sensors fail...
arXiv Machine Learning
Jun 18
TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults
arXiv:2606. 18539v1 Announce Type: new Abstract: Time series forecasting (TSF) underpins consequential decisions in energy, transportation, finance, and healthcare, yet TSF models are almost universally ranked by a single number (e.
arXiv AI
Oct 5
Time Series Forecasting Benchmarks Need Scenario-Grounded Stress Testing
The paper argues that current time‑series forecasting benchmarks are too narrow, rewarding low held‑out error while failing to capture real‑world failure modes such as structured input anomalies, regime shifts, and sensor faults. It proposes scenario‑grounded stress testing, where each test includes historical data, future targets, a semantic scenario, a failure operator, and a difficulty level, to make evaluation interpretable and deployment‑relevant.
arXiv Machine Learning
Oct 21
Robust Residual Correction via Selective Deployment for Time Series Forecasting
arXiv:2512.22428v3 Announce Type: replace Abstract: Post-hoc residual correction can reduce forecasting error, but always-on updates often fail to generalize: when residual signals are weak or noisy,...
arXiv Machine Learning
1d ago
Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data
arXiv:2611.06194v1 Announce Type: new Abstract: Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced. A few asse...