Grasynda: Graph-based Synthetic Time Series Generation
Fuente:
arXiv
Saved in:
| Main Authors: | Amorim, Luis, Santos, Moises, Azevedo, Paulo J., Soares, Carlos, Cerqueira, Vitor |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
by: Leites, José, et al.
Published: (2024)
by: Leites, José, et al.
Published: (2024)
Time Series Data Augmentation as an Imbalanced Learning Problem
by: Cerqueira, Vitor, et al.
Published: (2024)
by: Cerqueira, Vitor, et al.
Published: (2024)
Online Data Augmentation for Forecasting with Deep Learning
by: Cerqueira, Vitor, et al.
Published: (2024)
by: Cerqueira, Vitor, et al.
Published: (2024)
Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation
by: Santos, Moisés, et al.
Published: (2023)
by: Santos, Moisés, et al.
Published: (2023)
ModelRadar: Aspect-based Forecast Evaluation
by: Cerqueira, Vitor, et al.
Published: (2025)
by: Cerqueira, Vitor, et al.
Published: (2025)
L-GTA: Latent Generative Modeling for Time Series Augmentation
by: Roque, Luis, et al.
Published: (2025)
by: Roque, Luis, et al.
Published: (2025)
Forecasting with Deep Learning: Beyond Average of Average of Average Performance
by: Cerqueira, Vitor, et al.
Published: (2024)
by: Cerqueira, Vitor, et al.
Published: (2024)
N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
by: Matos, Ricardo, et al.
Published: (2025)
by: Matos, Ricardo, et al.
Published: (2025)
Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
by: Roque, Luis, et al.
Published: (2024)
by: Roque, Luis, et al.
Published: (2024)
Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction
by: Cerqueira, Vitor, et al.
Published: (2022)
by: Cerqueira, Vitor, et al.
Published: (2022)
Meta-learning and Data Augmentation for Stress Testing Forecasting Models
by: Inácio, Ricardo, et al.
Published: (2024)
by: Inácio, Ricardo, et al.
Published: (2024)
Simulating Biases for Interpretable Fairness in Offline and Online Classifiers
by: Inácio, Ricardo, et al.
Published: (2025)
by: Inácio, Ricardo, et al.
Published: (2025)
TSGDiff: Rethinking Synthetic Time Series Generation from a Pure Graph Perspective
by: Shen, Lifeng, et al.
Published: (2025)
by: Shen, Lifeng, et al.
Published: (2025)
Synthetic Time Series Generation via Complex Networks
by: Vale, Jaime, et al.
Published: (2026)
by: Vale, Jaime, et al.
Published: (2026)
Exploring Transformer Placement in Variational Autoencoders for Tabular Data Generation
by: Silva, Aníbal, et al.
Published: (2026)
by: Silva, Aníbal, et al.
Published: (2026)
Generating Synthetic Time Series Data for Cyber-Physical Systems
by: Sommers, Alexander, et al.
Published: (2024)
by: Sommers, Alexander, et al.
Published: (2024)
Synthetic Series-Symbol Data Generation for Time Series Foundation Models
by: Wang, Wenxuan, et al.
Published: (2025)
by: Wang, Wenxuan, et al.
Published: (2025)
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series
by: Nikitin, Alexander, et al.
Published: (2023)
by: Nikitin, Alexander, et al.
Published: (2023)
Tabular data generation with tensor contraction layers and transformers
by: Silva, Aníbal, et al.
Published: (2024)
by: Silva, Aníbal, et al.
Published: (2024)
Graphint: Graph-based Time Series Clustering Visualisation Tool
by: Boniol, Paul, et al.
Published: (2025)
by: Boniol, Paul, et al.
Published: (2025)
TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation
by: Deng, Bowen, et al.
Published: (2025)
by: Deng, Bowen, et al.
Published: (2025)
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models
by: Tian, Muhang, et al.
Published: (2023)
by: Tian, Muhang, et al.
Published: (2023)
Enabling Granular Subgroup Level Model Evaluations by Generating Synthetic Medical Time Series
by: Ibrahim, Mahmoud, et al.
Published: (2025)
by: Ibrahim, Mahmoud, et al.
Published: (2025)
TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
by: Taga, Ege Onur, et al.
Published: (2025)
by: Taga, Ege Onur, et al.
Published: (2025)
A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data
by: Loni, Mohammad, et al.
Published: (2024)
by: Loni, Mohammad, et al.
Published: (2024)
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
by: Talukder, Sabera, et al.
Published: (2024)
by: Talukder, Sabera, et al.
Published: (2024)
GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization
by: Van Langendonck, Louis, et al.
Published: (2025)
by: Van Langendonck, Louis, et al.
Published: (2025)
On Quantum Natural Policy Gradients
by: Sequeira, André, et al.
Published: (2024)
by: Sequeira, André, et al.
Published: (2024)
Trainability issues in quantum policy gradients
by: Sequeira, André, et al.
Published: (2024)
by: Sequeira, André, et al.
Published: (2024)
Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
by: Vapsi, Annita, et al.
Published: (2026)
by: Vapsi, Annita, et al.
Published: (2026)
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
by: Grieger, Niklas, et al.
Published: (2024)
by: Grieger, Niklas, et al.
Published: (2024)
STEB: In Search of the Best Evaluation Approach for Synthetic Time Series
by: Stenger, Michael, et al.
Published: (2025)
by: Stenger, Michael, et al.
Published: (2025)
$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
by: Boniol, Paul, et al.
Published: (2025)
by: Boniol, Paul, et al.
Published: (2025)
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
by: Cini, Andrea, et al.
Published: (2023)
by: Cini, Andrea, et al.
Published: (2023)
RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
by: Roque, Luis, et al.
Published: (2024)
by: Roque, Luis, et al.
Published: (2024)
Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs
by: Du, Shaoshuai, et al.
Published: (2026)
by: Du, Shaoshuai, et al.
Published: (2026)
Temporal Graph ODEs for Irregularly-Sampled Time Series
by: Gravina, Alessio, et al.
Published: (2024)
by: Gravina, Alessio, et al.
Published: (2024)
Graph Anomaly Detection in Time Series: A Survey
by: Ho, Thi Kieu Khanh, et al.
Published: (2023)
by: Ho, Thi Kieu Khanh, et al.
Published: (2023)
Graph-Aware Contrasting for Multivariate Time-Series Classification
by: Wang, Yucheng, et al.
Published: (2023)
by: Wang, Yucheng, et al.
Published: (2023)
FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation
by: Bai, Jun, et al.
Published: (2026)
by: Bai, Jun, et al.
Published: (2026)
Similar Items
-
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
by: Leites, José, et al.
Published: (2024) -
Time Series Data Augmentation as an Imbalanced Learning Problem
by: Cerqueira, Vitor, et al.
Published: (2024) -
Online Data Augmentation for Forecasting with Deep Learning
by: Cerqueira, Vitor, et al.
Published: (2024) -
Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation
by: Santos, Moisés, et al.
Published: (2023) -
ModelRadar: Aspect-based Forecast Evaluation
by: Cerqueira, Vitor, et al.
Published: (2025)