10 citations · 11 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting
Ruixin Ding, Yuqi Chen, Yu-Ting Lan +1
Long-term time series forecasting (LTSF) has been widely applied in finance, traffic prediction, and other domains. Recently, patch-based transformers have emerged as a promising a…