6 papers
SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning
Tengxue Zhang, Biao Ouyang, Yang Shu +3
Pre-trained models exhibit strong generalization to various downstream tasks. However, given the numerous models available in the model hub, identifying the most suitable one by in…
XLinear: A Lightweight and Accurate MLP-Based Model for Long-Term Time Series Forecasting with Exogenous Inputs
Xinyang Chen, Huidong Jin, Yu Huang +1
Despite the prevalent assumption of uniform variable importance in long-term time series forecasting models, real world applications often exhibit asymmetric causal relationships a…
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…
MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments
Zhengsheng Guo, Linwei Zheng, Xinyang Chen +3
While human cognition inherently retrieves information from diverse and specialized knowledge sources during decision-making processes, current Retrieval-Augmented Generation (RAG)…