9 papers
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting
Yangze Li, Zhengyang Zhou, Qihe Huang +3
Long-term time series forecasting requires temporal dependency modeling that remains stable and generalizable under temporal distribution shifts. However, real-world time series ar…
PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting
Jiaming Ma, Qihe Huang, Haofeng Ma +6
While existing multivariate time series forecasting models have advanced significantly in modeling periodicity, they largely neglect the periodic heterogeneity common in real-world…
To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series
Jiaming Ma, Siyuan Mu, Ruilin Tang +6
The prevailing Direct Forecasting (DF) paradigm dominates Long-term Time Series Forecasting (LTSF) by forcing models to predict the entire future horizon in a single forward pass.…
Talk2Image: A Multi-Agent System for Multi-Turn Image Generation and Editing
Shichao Ma, Yunhe Guo, Jiahao Su +3
Text-to-image generation tasks have driven remarkable advances in diverse media applications, yet most focus on single-turn scenarios and struggle with iterative, multi-turn creati…
QuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecasting
Shichao Ma, Zhengyang Zhou, Qihe Huang +2
Accurate forecasting of sequential data streams is a cornerstone of modern Web services, supporting applications such as traffic management, user behavior modeling, and online anom…
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Jiayue Liu, Zhongchao Yi, Zhengyang Zhou +4
Discovering regularities from spatiotemporal systems can benefit various scientific and social planning. Current spatiotemporal learners usually train an independent model from a s…
Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
Zhongchao Yi, Zhengyang Zhou, Qihe Huang +4
Spatiotemporal learning has become a pivotal technique to enable urban intelligence. Traditional spatiotemporal models mostly focus on a specific task by assuming a same distributi…
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization
Gengyu Lin, Zhengyang Zhou, Qihe Huang +3
Spatiotemporal learning plays a crucial role in mobile computing techniques to empower smart cites. While existing research has made great efforts to achieve accurate predictions o…
ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution
Zhengyang Zhou, Qihe Huang, Binwu Wang +4
Spatiotemporal (ST) learning has become a crucial technique to enable smart cities and sustainable urban development. Current ST learning models capture the heterogeneity via vario…