Time Series Generation
36 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Time Series Generation
Monopedal Gaits
Most implemented papers
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa.
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks.
Time-series Transformer Generative Adversarial Networks
We consider limitations posed specifically on time-series data and present a model that can generate synthetic time-series which can be used in place of real data.
TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation
abudesai/timeVAE
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Such interpretability can be highly advantageous in applications requiring transparency of model outputs or where users desire to inject prior knowledge of time-series patterns into the generative model.
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
MichaelMurashov/ecg-testing
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Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data.
Conditional Sig-Wasserstein GANs for Time Series Generation
SigCGANs/Conditional-Sig-Wasserstein-GANs
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The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterises the law of the time-series model.
MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial Networks
MTSS-GAN is a new generative adversarial network (GAN) developed to simulate diverse multivariate time series (MTS) data with finance applications in mind.
Vector Quantized Time Series Generation with a Bidirectional Prior Model
ml4its/timevqvae
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Time series generation (TSG) studies have mainly focused on the use of Generative Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants.
TimEHR: Image-based Time Series Generation for Electronic Health Records
esl-epfl/timehr
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Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality.
Monte Carlo Simulation of SDEs using GANs
JorinovanRhijn/master-thesis-gans-for-sdes
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We compare the input-output map obtained with the standard GAN and supervised GAN and show experimentally that the standard GAN may fail to provide a path-wise approximation.
