Cover Image

TSGDiff: Rethinking Synthetic Time Series Generation from a Pure Graph Perspective

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shen, Lifeng, Li, Xuyang, Long, Lele
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Grasynda: Graph-based Synthetic Time Series Generation
by: Amorim, Luis, et al.
Published: (2026)

Caformer: Rethinking Time Series Analysis from Causal Perspective
by: Zhang, Kexuan, et al.
Published: (2024)

SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
by: Yan, Qi, et al.
Published: (2023)

Rethinking Time Series Domain Generalization via Structure-Stratified Calibration
by: Li, Jinyang, et al.
Published: (2026)

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
by: Sun, Henan, et al.
Published: (2025)

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
by: Wang, Wenxuan, et al.
Published: (2025)

Synthetic Time Series Generation via Complex Networks
by: Vale, Jaime, et al.
Published: (2026)

Finding Time Series Anomalies using Granular-ball Vector Data Description
by: Shen, Lifeng, et al.
Published: (2025)

Periodic Graph-Enhanced Multivariate Time Series Anomaly Detector
by: Li, Jia, et al.
Published: (2025)

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
by: Zhao, Lifan, et al.
Published: (2024)

Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective
by: Wang, Peiyao, et al.
Published: (2026)

Generating Synthetic Time Series Data for Cyber-Physical Systems
by: Sommers, Alexander, et al.
Published: (2024)

Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
by: Wang, Chengsen, et al.
Published: (2024)

TSLANet: Rethinking Transformers for Time Series Representation Learning
by: Eldele, Emadeldeen, et al.
Published: (2024)

Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective
by: Hsieh, Din-Yin, et al.
Published: (2024)

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
by: Li, Xuyang, et al.
Published: (2025)

Be Wary of Your Time Series Preprocessing
by: Ennadir, Sofiane, et al.
Published: (2026)

TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series
by: Nikitin, Alexander, et al.
Published: (2023)

GraphThought: Graph Combinatorial Optimization with Thought Generation
by: Huang, Zixiao, et al.
Published: (2025)

UnitNorm: Rethinking Normalization for Transformers in Time Series
by: Huang, Nan, et al.
Published: (2024)

Multi-modal Time Series Analysis: A Tutorial and Survey
by: Jiang, Yushan, et al.
Published: (2025)

Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective
by: Ji, Qirui, et al.
Published: (2023)

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
by: Zhang, Lingzheng, et al.
Published: (2024)

Rethinking Graph Domain Adaptation: A Spectral Contrastive Perspective
by: Zhang, Haoyu, et al.
Published: (2025)

TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation
by: Deng, Bowen, et al.
Published: (2025)

Rethinking Tokenization for Clinical Time Series: When Less is More
by: Attrach, Rafi Al, et al.
Published: (2025)

Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
by: Liu, Haoxin, et al.
Published: (2026)

Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
by: Liu, Xvyuan, et al.
Published: (2025)

On Foundation Models for Dynamical Systems from Purely Synthetic Data
by: Ziegler, Martin, et al.
Published: (2024)

Pure Exploration with Feedback Graphs
by: Russo, Alessio, et al.
Published: (2025)

VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations
by: Venkatesan, Madhumitha, et al.
Published: (2026)

Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective
by: Ganev, Georgi, et al.
Published: (2026)

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations
by: Si, Haotian, et al.
Published: (2025)

Beyond Random Missingness: Clinically Rethinking for Healthcare Time Series Imputation
by: Qian, Linglong, et al.
Published: (2024)

Timer: Generative Pre-trained Transformers Are Large Time Series Models
by: Liu, Yong, et al.
Published: (2024)

BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series
by: Du, Guikang, et al.
Published: (2026)

SynTSBench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Series
by: Tan, Qitai, et al.
Published: (2025)

PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
by: Qi, Shiyi, et al.
Published: (2024)

FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation
by: Bai, Jun, et al.
Published: (2026)

Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalization
by: Shen, Li, et al.
Published: (2026)