5 papers · 1 filter
A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting
Xiaoyu Tao, Mingyue Cheng, Ze Guo +4
Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess predicti…
MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
Xiaoyu Tao, Mingyue Cheng, Ze Guo +4
Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications. Recently, large language model (LLM)- based forecasters have made promising…
Latent Shadows: The Gaussian-Discrete Duality in Masked Diffusion
Guinan Chen, Xunpeng Huang, Ying Sun +3
Masked discrete diffusion is a dominant paradigm for high-quality language modeling where tokens are iteratively corrupted to a mask state, yet its inference efficiency is bottlene…
From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization
Xiaoyu Tao, Shilong Zhang, Mingyue Cheng +5
Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent adv…
Confidence-Aware Self-Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Yanxi Luo, Shijin Wang, Zhongxing Xu +3
Multimodal sentiment analysis (MSA) aims to understand human sentiment through multimodal data. In real-world scenarios, practical factors often lead to uncertain modality missingn…