1 citations · 1 across the 7 of their papers we have counts for
11 papers
Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision
Tian Lan, Yifei Gao, Yimeng Lu +5
Whether a time-series pattern is anomalous often depends on the operating regime of the monitored process. A missing event can signal a fault in one regime and be routine in anothe…
Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection
Tian Lan, Yifei Gao, Yimeng Lu +6
Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of normality and favor different det…
Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing
Yifei Gao, Tian Lan, Yimeng Lu +5
Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing meth…
VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection
Yingyuan Yang, Tian Lan, Yifei Gao +6
Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundation models face a fundamental tr…
AXIS: Explainable Time Series Anomaly Detection with Large Language Models
Tian Lan, Hao Duong Le, Jinbo Li +4
Time-series anomaly detection (TSAD) increasingly demands explanations that articulate not only if an anomaly occurred, but also what pattern it exhibits and why it is anomalous. L…
Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
Tian Lan, Hao Duong Le, Jinbo Li +4
Time series anomaly detection (TSAD) is a critical task, but developing models that generalize to unseen data in a zero-shot manner remains challenging. Existing foundation models…
Wavelet-based Disentangled Adaptive Normalization for Non-stationary Times Series Forecasting
Junpeng Lin, Tian Lan, Bo Zhang +6
Forecasting non-stationary time series is a challenging task because their statistical properties often change over time, making it hard for deep models to generalize well. Instanc…
Tensor State Space-based Dynamic Multilayer Network Modeling
Tian Lan, Jie Guo, Chen Zhang
Understanding the complex interactions within dynamic multilayer networks is critical for advancements in various scientific domains. Existing models often fail to capture such net…
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series
Tian Lan, Yifei Gao, Yimeng Lu +1
Unsupervised Time series anomaly detection plays a crucial role in applications across industries. However, existing methods face significant challenges due to data distributional…
TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents
Xiaochuan Gou, Ziyue Li, Tian Lan +6
Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the pred…
MultiFun-DAG: Multivariate Functional Directed Acyclic Graph
Tian Lan, Ziyue Li, Junpeng Lin +6
Directed Acyclic Graphical (DAG) models efficiently formulate causal relationships in complex systems. Traditional DAGs assume nodes to be scalar variables, characterizing complex…