Time Series Generation
22 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Time Series Generation
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.
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.
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.
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.
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.
Sig-Wasserstein GANs for Time Series Generation
sigcgans/sig-wasserstein-gans
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Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines.
Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks
This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center.
