6 papers
Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling
Yang Zhao, Peisong Niu, Tian Zhou +5
The development of 0.1 global weather forecasting models based on machine learning (ML) is constrained by the limited availability of high-resolution data, as decades of…
Learning Video Dynamics with Predictive Differentiable Rendering
Yujin Tang, Tian Zhou, Xin Lin +5
How to accurately predict a high-fidelity future world? While the visual world is inherently continuous, existing deterministic video prediction models operate in discrete pixel sp…
Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou +4
Although contrastive and other representation-learning methods have long been explored in vision and NLP, their adoption in modern time series forecasters remains limited. We belie…
Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
Linxiao Yang, Xue Jiang, Gezheng Xu +9
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted featur…
Utilizing Strategic Pre-training to Reduce Overfitting: Baguan -- A Pre-trained Weather Forecasting Model
Peisong Niu, Ziqing Ma, Tian Zhou +4
Weather forecasting has long posed a significant challenge for humanity. While recent AI-based models have surpassed traditional numerical weather prediction (NWP) methods in globa…
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach
Chao Chen, Tian Zhou, Yanjun Zhao +3
Spatio-temporal forecasting is crucial in various fields and requires a careful balance between identifying subtle patterns and filtering out noise. Vector quantization (VQ) appear…