Hugging Face Trending Papers August 3, 2026

Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System

Read the original on Hugging Face Trending Papers →

Hardware error logs in high-performance computing (HPC) systems provide early signals of abnormal behavior, yet there remain challenges in effectively forecasting these errors using modern predictive methods. This work investigates the boundaries of applying time series forecasting to HPC hardware error dynamics.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

llms

arXiv Machine Learning

Aug 4

Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System

arXiv:2608. 01648v1 Announce Type: new Abstract: Hardware error logs in high-performance computing (HPC) systems provide early signals of abnormal behavior, yet there remain challenges in effectively forecasting these errors using modern predictive methods.

By Kaiyuan Liao, Xiwei Xuan, Tanwi Mallick, Kevin Brown, Christopher D. Carothers, Kwan-Liu Ma

llms

arXiv Machine Learning

Jun 2

Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification

arXiv:2602. 16224v2 Announce Type: replace Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal data distribution, leading to training instability or convergence to poor local minima.

By Xu Zhang, Peng Wang, Yichen Li, Wei Wang

safety

arXiv Machine Learning

Jun 2

Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting

arXiv:2512. 22702v2 Announce Type: replace Abstract: Deep learning models have grown popular in time series applications.

By Valentina Moretti, Ivan Marisca, Cesare Alippi, Andrea Cini

benchmarks

arXiv Machine Learning

Jul 20

SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction

arXiv:2602. 04643v2 Announce Type: replace Abstract: Time-series anomaly prediction aims to forecast future system failures before they fully emerge, making latent predictive models such as JEPA a promising framework for capturing precursor dynamics.

By Yanan He, Yunshi Wen, Xin Wang, Tengfei Ma

diffusion efficiency benchmarks

arXiv Machine Learning

Aug 4

AROpt: An Optimization Method for Autoregressive Time Series Forecasting

arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.

By Zheng Li, Jerry Cheng, Huanying Gu

llms benchmarks

arXiv Machine Learning

Jun 10

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.

By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman

llms benchmarks safety

Related stories

Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System

Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification

Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting

SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction

AROpt: An Optimization Method for Autoregressive Time Series Forecasting

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data