2 citations · 5 across the 13 of their papers we have counts for
16 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…
FaLW: A Forgetting-aware Loss Reweighting for Long-tailed Unlearning
Liheng Yu, Zhe Zhao, Yuxuan Wang +4
Machine unlearning, which aims to efficiently remove the influence of specific data from trained models, is crucial for upholding data privacy regulations like the ``right to be fo…
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.…
A General ReLearner: Empowering Spatiotemporal Prediction by Re-learning Input-label Residual
Jiaming Ma, Binwu Wang, Pengkun Wang +3
Prevailing spatiotemporal prediction models typically operate under a forward (unidirectional) learning paradigm, in which models extract spatiotemporal features from historical ob…
We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification
Zhipeng Liu, Peibo Duan, Xuan Tang +6
The World Wide Web thrives on intelligent services that rely on accurate time series classification, which has recently witnessed significant progress driven by advances in deep le…
Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models
Zhiqing Cui, Binwu Wang, Qingxiang Liu +4
Large language models (LLM) have emerged as a promising avenue for time series forecasting, offering the potential to integrate multimodal data. However, existing LLM-based approac…
TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting
Zhipeng Liu, Peibo Duan, Xuan Tang +6
Although Transformers excel in natural language processing, their extension to time series forecasting remains challenging due to insufficient consideration of the differences betw…
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…
DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification
Zhipeng Liu, Peibo Duan, Binwu Wang +5
Real-world time series typically exhibit complex temporal variations, making the time series classification task notably challenging. Recent advancements have demonstrated the pote…
Spatiotemporal Causal Decoupling Model for Air Quality Forecasting
Jiaming Ma, Guanjun Wang, Sheng Huang +4
Due to the profound impact of air pollution on human health, livelihoods, and economic development, air quality forecasting is of paramount significance. Initially, we employ the c…
A baseline for machine-learning-based hepatocellular carcinoma diagnosis using multi-modal clinical data
Binwu Wang, Isaac Rodriguez, Leon Breitinger +9
The objective of this paper is to provide a baseline for performing multi-modal data classification on a novel open multimodal dataset of hepatocellular carcinoma (HCC), which incl…
FusionTransNet for Smart Urban Mobility: Spatiotemporal Traffic Forecasting Through Multimodal Network Integration
Binwu Wang, Yan Leng, Guang Wang +1
This study develops FusionTransNet, a framework designed for Origin-Destination (OD) flow predictions within smart and multimodal urban transportation systems. Urban transportation…
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…
Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective
Xu Wang, Pengfei Gu, Pengkun Wang +4
Spatiotemporal learning, which aims at extracting spatiotemporal correlations from the collected spatiotemporal data, is a research hotspot in recent years. And considering the inh…
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics
Zhengyang Zhou, Yang Kuo, Wei Sun +4
Spatiotemporal forecasting is an imperative topic in data science due to its diverse and critical applications in smart cities. Existing works mostly perform consecutive prediction…