1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting
Xinxing Zhou, Jiaqi Ye, Shubao Zhao +6
In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning. Traditional foreca…