Chen-Juan Guo

TFB-2: benchmarking and automated ensemble for time series forecasting

Adaptive Spectral-Koopman Dynamics Modeling for Temporal Domain Generalization

Temporal Domain Generalization (TDG) has emerged to address real-world streaming data with distribution shifts over time. However, existing methods are either prone to overfitting to domain-specific noise in the data space or become overly complex and less interpretable in the parameter space. To bridge these gaps, we...

Multi-View Ensemble for Time Series Anomaly Detection via Coupling Flows

Time series anomaly detection faces a critical challenge that different anomaly types require different detection mechanisms, yet single methods are inherently limited by their design biases. We propose FlowFuse, a multi-view ensemble framework with coupling flow-based score fusion for time series anomaly detection. Fl...

XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching

XMatch (EXogenous MATCHing), a covariate-aware forecasting model that realizes a strategy that matches future and historical exogenous patterns and uses the corresponding endogenous patterns to enhance forecasting through a tree-structured matching process that adaptively adjusts the number of exogenous variables used...

QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities

The proposed QiYao-M is a role-aware multimodal TSFM that models the two types of modalities separately, including endogenous and exogenous modalities, and introduces Endo-Modality Proxy Training to train this retrieval module without exogenous multimodal pretraining data.

SIFT: Enhancing Time Series Foundation Models via Semantic Invariance and Structural Fidelity Fine-Tuning

This work proposes SIFT, a robust adaptation method that enhances time series foundation models by preserving Semantic Invariance and structural Fidelity throughout the fine-Tuning process, and implements a component-based structural fidelity enhancement.

BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting

BITS, a standardized, reproducible, and extensible benchmark for advancing research on irregular time series forecasting, is proposed and it is found that method performance varies substantially across irregularity characteristics, with no single modeling strategy consistently dominating.

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting

In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting tasks via generative probabilistic modeling, while ensuring both efficiency and robustness. FLAME utilizes the Legendre Memory for strong generalization capabilities. By ada...

WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting

This work proposes WorldTS, a world-modeling based forecasting framework that integrates multimodal covariates directly into the forecasting to further improve forecasting performance and employs a two-stage training strategy.

Aurora-X: Built for Extreme Time Series Forecasting

A novel pattern-guided mixture-of-experts that expands model capacity through sparse activation and uses shallow patch similarities to constrain deep-layer routing, guiding expert specialization across heterogeneous time series and an implicit quantile network head that predicts arbitrary quantiles to characterize pred...

A Selective Multi-View Representation Augmentation Framework for Crystal Property Prediction

The notion of a crystal supercell is introduced, which enables more comprehensive explorations of crystal structure and offers evidence that SILVA is capable of outperforming state-of-the-art methods.

ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies

ST-EVO is proposed, which supports dialogue-wise communication scheduling with a compact yet powerful flow-matching based Scheduler to make precise Spatio-Temporal scheduling, and can also perceive the uncertainty of MAS, and possesses self-feedback ability to learn from accumulated experience.