1 citations · 1 across the 2 of their papers we have counts for
4 papers
Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models
Qingxiang Liu, Zhiqing Cui, Xiaoliang Luo +7
The underperformance of existing multimodal large language models for time series reasoning lies in the absence of rationale priors that connect temporal observations to their down…
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…
OccamVTS: Distilling Vision Models to 1% Parameters for Time Series Forecasting
Sisuo Lyu, Siru Zhong, Weilin Ruan +4
Time series forecasting is fundamental to diverse applications, with recent approaches leverage large vision models (LVMs) to capture temporal patterns through visual representatio…
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Yuxuan Liang +6
Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of r…