5 papers
Boosting AI Reliability with an FSM-Driven Streaming Inference Pipeline: An Industrial Case
Yutian Zhang, Zhongyi Pei, Yi Mao +3
The widespread adoption of AI in industry is often hampered by its limited robustness when faced with scenarios absent from training data, leading to prediction bias and vulnerabil…
Thoth: Mid-Training Bridges LLMs to Time Series Understanding
Jiafeng Lin, Yuxuan Wang, Jialong Wu +3
Large Language Models (LLMs) have demonstrated remarkable success in general-purpose reasoning. However, they still struggle to understand and reason about time series data, which…
Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models
Yunzhong Qiu, Zhiyao Cen, Zhongyi Pei +2
Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real-world time…
DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models
Jinpeng Li, Zhongyi Pei, Huaze Xue +3
Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasti…
TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
Jiafeng Lin, Yuxuan Wang, Huakun Luo +2
Multimodal time series forecasting has garnered significant attention for its potential to provide more accurate predictions than traditional single-modality models by leveraging r…