3 papers
cs.LG2026
LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
Alexander Chemeris, Ming Jin, Randall Balestriero
Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often d…
cs.LG2026
EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
Xinxing Zhou, Qingren Yao, Yiji Zhao +5
Most time series foundation models are pretrained by directly predicting future observations, which often yields weakly structured latent representations that capture surface noise…
cs.LG2024
EffiCANet: Efficient Time Series Forecasting with Convolutional Attention
Xinxing Zhou, Jiaqi Ye, Shubao Zhao +4
The exponential growth of multivariate time series data from sensor networks in domains like industrial monitoring and smart cities requires efficient and accurate forecasting mode…