10 citations · 13 across the 16 of their papers we have counts for
11 papers · 1 filter
SemPSG: A Semantic Channel-Aware Foundation Model for Polysomnography Analysis
Junyu Chen, Chenxi Liu, Shiqin Tang +5
Polysomnography (PSG) integrates multiple physiological signals to provide a comprehensive characterization of human sleep, yet its heterogeneous channel configurations across cent…
Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework
Delun Kong, Wanyun Ling, Chenxi Liu +1
Zero-shot time-series forecasting (TSF) is often described as forecasting without target-specific parameter updates, but that training-status condition does not specify what eviden…
UHI-Bench: Benchmarking Dual-Source Urban Heat Island Modeling Across Cities in Diverse Climate Regimes
Wanyun Ling, Chenxi Liu, Yi Xie +3
Urban heat islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks. Their two primary observations, land surface temperature UHI (LST-UHI) and nea…
Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language
Xinran Feng, Yi Xie, Chao Zhang +4
Training multimodal models to align time series with language runs into a self-supervision trap. The usual recipe asks an LLM to read a series and write a description, so label qua…
Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising
Kangjia Yan, Chenxi Liu, Hao Miao +4
Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices. However, the volume of time series data may vary sig…
LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions
Chenxi Liu, Hao Miao, Cheng Long +3
Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and tim…
Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation
Chenxi Liu, Hao Miao, Qianxiong Xu +5
Multivariate time series forecasting (MTSF) endeavors to predict future observations given historical data, playing a crucial role in time series data management systems. With adva…
Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era
Chenxi Liu, Shaowen Zhou, Qianxiong Xu +4
The proliferation of edge devices has generated an unprecedented volume of time series data across different domains, motivating various well-customized methods. Recently, Large La…
TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment
Chenxi Liu, Qianxiong Xu, Hao Miao +5
Multivariate time series forecasting (MTSF) aims to learn temporal dynamics among variables to forecast future time series. Existing statistical and deep learning-based methods suf…
LightTR: A Lightweight Framework for Federated Trajectory Recovery
Ziqiao Liu, Hao Miao, Yan Zhao +3
With the proliferation of GPS-equipped edge devices, huge trajectory data is generated and accumulated in various domains, motivating a variety of urban applications. Due to the li…
Spatial-Temporal Large Language Model for Traffic Prediction
Chenxi Liu, Sun Yang, Qianxiong Xu +4
Traffic prediction, an essential component for intelligent transportation systems, endeavours to use historical data to foresee future traffic features at specific locations. Altho…