6 papers
Image Synthesis as an Intermediate for Controllable Time Series Generation
Haochen Yuan, Jing Xie, Yunbo Wang
Semantic-driven time-series generation offers a promising way to improve downstream learning in few-shot forecasting, but directly generating numerical sequences from language ofte…
Zero-Shot Time-Series Question Answering via Decoupled Perception and Reasoning
Jing Xie, Haochen Yuan, Yunbo Wang
Time-series question answering (TSQA) requires grounding linguistic queries and diverse answer formats in complex numerical observations. However, existing methods heavily overfit…
Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
Haochen Yuan, Yichen Song, Yunbo Wang +1
Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, whi…
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization
Haochen Yuan, Minting Pan, Yunbo Wang +3
Reinforcement learning (RL) has shown significant promise for sequential portfolio optimization tasks, such as stock trading, where the objective is to maximize cumulative returns…
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
Haochen Yuan, Yutong Wang, Yihong Chen +2
Time series forecasting, particularly in few-shot learning scenarios, is challenging due to the limited availability of high-quality training data. To address this, we present a pi…
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video
Xiangming Zhu, Huayu Deng, Haochen Yuan +2
We introduce latent intuitive physics, a transfer learning framework for physics simulation that can infer hidden properties of fluids from a single 3D video and simulate the obser…