2 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
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…