3 papers
cs.AI2026
From Scalars to Time Series: Rethinking Implicit Neural Representations for Time-Varying Volumetric Data
Weihan Zhang, Xuan Zhao, Yenwen Peng +2
Implicit neural representations (INRs) for time-varying volumetric data are typically trained using dense sampling over spatiotemporal coordinates, where each observation correspon…
cs.LG2026
PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting
Tian Sun, Yuqi Chen, Weiwei Sun
Despite advances in the Transformer architecture, their effectiveness for long-term time series forecasting (LTSF) remains controversial. In this paper, we investigate the potentia…
cs.LG2025
Learning Spatio-Temporal Dynamics for Trajectory Recovery via Time-Aware Transformer
Tian Sun, Yuqi Chen, Baihua Zheng +1
In real-world applications, GPS trajectories often suffer from low sampling rates, with large and irregular intervals between consecutive GPS points. This sparse characteristic pre…