37 citations · 43 across the 24 of their papers we have counts for
15 papers · 1 filter
EVOLVE: Uncertainty-Aware Multivariate Time Series Anomaly Prediction with Adaptive Prototype Memories
Jianxin Jin, Kai Zhao, Shiyan Hu +8
Unsupervised multivariate time series anomaly prediction (AP) aims to estimate future anomaly risks from current observations, providing early-warning signals for proactive interve…
BEACON: Bridging Multimodal Semantics and Asynchronous Propagation for Cloud System Anomaly Detection
Zhe Li, Tengxue Zhang, Yang Shu +4
Modern large-scale cloud systems continuously generate massive and multimodal observability data, such as time-series metrics, log records, and system dependency graphs, which refl…
ORBIT: Autoregressive Time Series Generation via Next Frequency-Band Prediction
Chunyi Hou, Xiangfei Qiu, Hanyin Cheng +5
Deep generative models provide an effective modern solution to alleviating the problem of data scarcity by synthesizing realistic time series. However, despite the considerable pro…
QiYao-I: A Manifold Based Foundation Model for Irregular Multivariate Time Series Forecasting
Linfeng Wang, Ruitong Zhang, Kai Zhao +7
Irregular multivariate time series forecasting is a challenging yet important problem in real-world applications, where observations are often irregularly sampled and asynchronousl…
XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching
Ziyang Zhang, Hanyin Cheng, Xiangfei Qiu +3
Future exogenous variables provide valuable information for forecasting endogenous time series. Existing covariate-aware methods primarily learn the direct influence of exogenous v…
QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities
Hanyin Cheng, Linfeng Wang, Zhengbo Qu +7
Existing multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the distinct forecasting roles of…
Aurora-X: Built for Extreme Time Series Forecasting
Xingjian Wu, Chenjuan Guo, Xiangfei Qiu +6
Time series foundation models (TSFMs) enable cross-domain forecasting, but their development as general-purpose forecasters remains constrained by underexplored training potential…
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…
Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining
Biao Ouyang, Tengxue Zhang, Zhihao Zhuang +3
Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typically rely on dataset-specific…
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…
Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction
Shiyan Hu, Jianxin Jin, Yang Shu +3
Time series anomaly detection plays a critical role in many dynamic systems. Despite its importance, previous approaches have primarily relied on unimodal numerical data, overlooki…
Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecasting
Siyuan Wang, Peng Chen, Yihang Wang +4
Existing time series forecasting methods primarily rely on the numerical data itself. However, real-world time series exhibit complex patterns associated with multimodal informatio…
Empowering Time Series Analysis with Large-Scale Multimodal Pretraining
Peng Chen, Siyuan Wang, Shiyan Hu +7
While existing time series foundation models primarily rely on large-scale unimodal pretraining, they lack complementary modalities to enhance time series understanding. Building m…
PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering
Junkai Lu, Peng Chen, Xingjian Wu +4
Time series reasoning demands both the perception of complex dynamics and logical depth. However, existing LLM-based approaches exhibit two limitations: they often treat time serie…
ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies
Xingjian Wu, Xvyuan Liu, Junkai Lu +6
LLM-powered Multi-Agent Systems (MAS) have emerged as an effective approach towards collaborative intelligence, and have attracted wide research interests. Among them, ``self-evolv…