1 citations · 1 across the 9 of their papers we have counts for
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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…
BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting
Kangjia Yan, Linfeng Wang, Tianen Shen +7
Despite recent progress in irregular time series forecasting, the field still lacks a unified benchmark for fair and comprehensive evaluation. Existing evaluations are often conduc…
FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification
Pingping Liu, Muyao Wang, Zijian Zhang +5
Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficien…
TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
Zhe Li, Jindong Tian, Hao Miao +3
Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring. Due to the diversity of…
TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
Mingyuan Xia, Chunxu Zhang, Zijian Zhang +4
Temporal non-stationarity, the phenomenon that time series distributions change over time, poses fundamental challenges to reliable time series forecasting. Intuitively, the comple…
ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting
Jindong Tian, Yifei Ding, Ronghui Xu +3
Weather forecasting is a fundamental task in spatiotemporal data analysis, with broad applications across a wide range of domains. Existing data-driven forecasting methods typicall…
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…
Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning
Kai Zhao, Zhihao Zhuang, Chenjuan Guo +3
Time series anomaly prediction plays an essential role in many real-world scenarios, such as environmental prevention and prompt maintenance of cyber-physical systems. However, exi…