Bin Yang

Learning Continuous and Discrete Dynamics for Time Series Anomaly Detection via Probabilistic Modeling

CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter

Aurora: Towards Universal Generative Multimodal Time Series Forecasting

ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting

Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction

GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables

Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecasting

CC-Time: Cross-Model and Cross-Modality Time Series Forecasting

RockTS: Robust Time Series Forecasting based on Information Bottleneck and Optimal Transport

SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning

Unlocking the Power of Mixture-of-Experts for Task-Aware Time Series Analytics

STAR: Boosting Time Series Foundation Models for Anomaly Detection Through State-aware Adapter

Towards a Multimodal Foundation Model for Time Series Analysis

Enhancing Offline-to-Online Reinforcement Learning by Adaptive Experience Aligned Diffusion Sampling

ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting

MSFlow: Fusing Multi-Perspective Scores through Coupling Flows for Time Series Anomaly Detection

TFD: Spectrally Guided Time–Frequency Diffusion Model For Time Series Imputation

Time Series Causal Discovery via Instance-specific Modeling and Intervention-based Pretraining

Deciphering Invariant Feature Decoupling in Source-free Time Series Forecasting with Proxy Denoising

Multi-Scale Spatial-Temporal Hypergraph Network with Lead-Lag Structures for Stock Time Series Forecasting

Learning to Factorize Spatio-Temporal Foundation Models

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting

Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective

CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling

DBLoss: Decomposition-based Loss Function for Time Series Forecasting

Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer

Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems

CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching

LightGTS: A Lightweight General Time Series Forecasting Model

Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation

Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders

ROSE: Register-Assisted General Time Series Forecasting with Decomposed Frequency Learning

DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone

Continuous Space-Time Video Super-Resolution via Event Camera

MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast

SEE: See Everything Every Time - Broader Light Range Image Enhancement via Events

FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting