221 citations · 296 across the 22 of their papers we have counts for
8 papers · 1 filter
Rethinking Time Series Tokenization from a Frequency Perspective
Zelong Tian, Ming Jin, Bo Du +1
Tokenization that partitions time series into subsequences has become a foundational paradigm in modern time series modeling. However, we find that current tokenization strategies…
Rethinking World Models for Biosignals
Xianglong Bao, Bosong Huang, Xiaoda Wang +4
World models promise to learn how systems evolve and respond to actions, enabling simulation, planning and decision-making. Bringing this promise to biosignals requires more than a…
TimeInteract: Towards Real-Time Interactive Intelligence for Streaming Time Series
Sheng Pan, Yongli Gu, Yiqing Guo +4
Real-world time series evolve continuously, with meaningful changes potentially emerging at any moment. However, existing time-series language models (TSLMs) remain inherently stat…
EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards
Zhiqing Cui, Xinxiang Yin, Yihong Tang +11
Earth-system analysis reconstructs changing physical processes from observations that differ in source, scale, timing, and modality. Natural hazards make this work consequential be…
LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
Sheng Pan, Ming Jin, Bo Du +1
Time series forecasting serves as an essential tool for many real-world applications, supporting tasks such as resource optimization and decision-making. Despite significant archit…
LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space
Bosong Huang, Panzhen Zhao, Zengxiang Li +5
Electrocardiography (ECG) is a cornerstone of cardiac assessment, making the learning of informative ECG representations fundamental to tasks ranging from disease diagnosis to clin…
TimeOmni-VL: Unified Models for Time Series Understanding and Generation
Tong Guan, Sheng Pan, Johan Barthelemy +5
Recent time series modeling faces a sharp divide between numerical generation and semantic understanding, with research showing that generation models often rely on superficial pat…
Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting
Zhiqing Cui, Siru Zhong, Ming Jin +3
Global air quality forecasting grapples with extreme spatial heterogeneity and the poor generalization of existing transductive models to unseen regions. To tackle this, we propose…