4 papers
LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Models
Jiacheng You, Jingcheng Yang, Yuhang Xie +7
Time-series forecasting in real-world applications such as finance and energy often faces challenges due to limited training data and complex, noisy temporal dynamics. Existing dee…
STransformer: Scalable Structured Transformers for Global Station Weather Forecasting
Hongyi Chen, Xiucheng Li, Xinyang Chen +4
Global Station Weather Forecasting (GSWF) is a key meteorological research area, critical to energy, aviation, and agriculture. Existing time series forecasting methods often ignor…
Towards Scalable and Structured Spatiotemporal Forecasting
Hongyi Chen, Xiucheng Li, Xinyang Chen +3
In this paper, we propose a novel Spatial Balance Attention block for spatiotemporal forecasting. To strike a balance between obeying spatial proximity and capturing global correla…
Handling Imbalanced Pseudolabels for Vision-Language Models with Concept Alignment and Confusion-Aware Calibrated Margin
Yuchen Wang, Xuefeng Bai, Xiucheng Li +3
Adapting vision-language models (VLMs) to downstream tasks with pseudolabels has gained increasing attention. A major obstacle is that the pseudolabels generated by VLMs tend to be…