7 papers
T-LLM: Teaching Large Language Models to Forecast Time Series via Temporal Distillation
Suhan Guo, Bingxu Wang, Shaodan Zhang +1
Time series forecasting plays a critical role in decision-making across many real-world applications. Unlike data in vision and language domains, time series data is inherently tie…
MiCA: A Mobility-Informed Causal Adapter for Lightweight Epidemic Forecasting
Suhan Guo, Jiahong Deng, Furao Shen
Accurate forecasting of infectious disease dynamics is critical for public health planning and intervention. Human mobility plays a central role in shaping the spatial spread of ep…
IPF-RDA: An Information-Preserving Framework for Robust Data Augmentation
Suorong Yang, Hongchao Yang, Suhan Guo +2
Data augmentation is widely utilized as an effective technique to enhance the generalization performance of deep models. However, data augmentation may inevitably introduce distrib…
Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks
Suhan Guo, Zhenghao Xu, Furao Shen +1
Accurate prediction of contagious disease outbreaks is vital for informed decision-making. Our study addresses the gap between machine learning algorithms and their epidemiological…
SPAT: Sensitivity-based Multihead-attention Pruning on Time Series Forecasting Models
Suhan Guo, Jiahong Deng, Mengjun Yi +2
Attention-based architectures have achieved superior performance in multivariate time series forecasting but are computationally expensive. Techniques such as patching and adaptive…
RAM: Replace Attention with MLP for Efficient Multivariate Time Series Forecasting
Suhan Guo, Jiahong Deng, Yi Wei +3
Attention-based architectures have become ubiquitous in time series forecasting tasks, including spatio-temporal (STF) and long-term time series forecasting (LTSF). Yet, our unders…