34 citations · 39 across the 7 of their papers we have counts for
9 papers
A Unified Scaling Law for Time Series Foundation Models
Xilin Dai, Yiding Liu, Zewei Dong +2
We develop a Unified Scaling Law and a Unified Theory of Time Series Learning to understand how model capacity and historical information support forecasting. Across different look…
Pythia: Toward Foundation World Models for Multimodal Time Series
Xilin Dai, Hongzhou Chen, Yifan Hu +3
Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide complementary information abo…
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
Yifan Hu, Xilin Dai, Zhiyuan Qu +4
Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning strategies, and intervention r…
Into the ORBIT for Time Series: Training Regimes for Foundation Models
Hongjie Xia, Yiding Liu, Yifan Hu +2
Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous corpora remain under-explored.…
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
Yifan Hu, Hongzhou Chen, Peiyuan Liu +3
Real-world time series are often highly incomplete and irregular due to sensor dormancy, transmission delays, and event-driven sampling, making reliable forecasting fundamentally c…
Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
Yiding Liu, Yifan Hu, Hongjie Xia +5
Time series foundation models (TSFMs) are transforming the forecasting paradigm through large-scale cross-domain pretraining. However, most existing TSFMs remain univariate, and re…
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Qianru Zhang, Zheng Wang, Cheng Long +5
Detecting anomalous trajectories has become an important task in many location-based applications. While many approaches have been proposed for this task, they suffer from various…
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning
Zheng Wang, Cheng Long, Gao Cong +1
Similar trajectory search is a fundamental problem and has been well studied over the past two decades. However, the similar subtrajectory search (SimSub) problem, aiming to return…
Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation
Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay +3
This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challen…