15 papers
SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering
Xingze Zheng, Hanyin Cheng, Siyuan Wang +4
Time series anomaly detection plays a crucial role in a wide range of real-world applications. Reconstruction-based methods have become the mainstream paradigm, but they suffer fro…
DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables
Xiangfei Qiu, Yuhan Zhu, Zhengyu Li +3
Time series forecasting is essential in various domains. Compared to relying solely on endogenous variables (i.e., target variables), considering exogenous variables (i.e., covaria…
Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting
Xiangfei Qiu, Liu Yang, Xiangyu Xu +9
Time series forecasting occurs in a range of financial applications providing essential decision-making support to investors, regulatory institutions, and analysts. Unlike multivar…
CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter
Hanyin Cheng, Xingjian Wu, Yang Shu +4
Most existing Time Series Foundation Models (TSFMs) use channel independent modeling and focus on capturing and generalizing temporal dependencies, while neglecting the correlation…
ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
Xvyuan Liu, Xiangfei Qiu, Hanyin Cheng +4
Irregular multivariate time series (IMTS) are prevalent in critical domains like healthcare and finance, where accurate forecasting is vital for proactive decision-making. However,…
FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
Xingjian Wu, Hanyin Cheng, Xiangfei Qiu +4
In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support both deterministic and probabilistic forecasting via ge…