6 papers
STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning
Juntong Ni, Shiyu Wang, Qi He +2
Spatio-temporal reasoning in time series involves the explicit synthesis of temporal dynamics, spatial dependencies, and textual context. This capability is vital for high-stakes d…
TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
Tong Guan, Zijie Meng, Dianqi Li +7
Recent advances in multimodal time series learning underscore a paradigm shift from analytics centered on basic patterns toward advanced time series understanding and reasoning. Ho…
EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge
Congcong Hu, Yuang Shi, Fan Huang +4
Demand forecasting is a cornerstone of e-commerce operations, directly impacting inventory planning and fulfillment scheduling. However, existing forecasting systems often fail dur…
Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
Yaxuan Kong, Yiyuan Yang, Shiyu Wang +7
Understanding time series data is fundamental to many real-world applications. Recent work explores multimodal large language models (MLLMs) to enhance time series understanding wi…
TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation
Juntong Ni, Zewen Liu, Shiyu Wang +2
Transformer-based and CNN-based methods demonstrate strong performance in long-term time series forecasting. However, their high computational and storage requirements can hinder l…
U-Cast: Learning Hierarchical Structures for High-Dimensional Time Series Forecasting
Juntong Ni, Shiyu Wang, Zewen Liu +4
Time series forecasting (TSF) is a central problem in time series analysis. However, as the number of channels in time series datasets scales to the thousands or more, a scenario w…