Yang Shu

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

Aurora: Towards Universal Generative Multimodal Time Series Forecasting

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

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

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

Towards a Multimodal Foundation Model for Time Series Analysis

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

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

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

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

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

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

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

Vision-Enhanced Time Series Forecasting by Decomposed Feature Extraction and Composed Reconstruction

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