37 citations · 47 across the 16 of their papers we have counts for
6 papers · 1 filter
InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
Mingyue Cheng, Xiaoyu Tao, Huajian Zhang +5
Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels. While effective, this paradigm…
Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs
Yitong Zhou, Yucong Luo, Mingyue Cheng +4
To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning archi…
Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
Xiaoyu Tao, Tingyue Pan, Mingyue Cheng +3
Time series classification plays a fundamental role in a wide range of real-world applications. Recently, large language models (LLMs) have demonstrated strong generalization and r…
Advancing Time Series Classification with Multimodal Language Modeling
Mingyue Cheng, Yiheng Chen, Qi Liu +2
For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier mod…
Generative Pretrained Hierarchical Transformer for Time Series Forecasting
Zhiding Liu, Jiqian Yang, Mingyue Cheng +2
Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Neverthel…
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
Mingyue Cheng, Qi Liu, Zhiding Liu +3
Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the…