88 citations · 88 across the 12 of their papers we have counts for
9 papers · 1 filter
Retrieval-Augmented Generation with Covariate Time Series
Kenny Ye Liang, Zhongyi Pei, Huan Zhang +3
While RAG has greatly enhanced LLMs, extending this paradigm to Time-Series Foundation Models (TSFMs) remains a challenge. This is exemplified in the Predictive Maintenance of the…
Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series
Jiafeng Lin, Mengren Zheng, Simeng Ye +5
Time series forecasting has witnessed an increasing demand across diverse industrial applications, where accurate predictions are pivotal for informed decision-making. Beyond numer…
Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
Yunzhong Qiu, Binzhu Li, Hao Wei +5
Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series founda…
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…
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
Zhongyi Pei, Zhiyao Cen, Yipeng Huang +4
Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-b…
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…