Time Series Generation

23 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

ratschlab/RGAN ICLR 2018

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

ykang/gratis 7 Mar 2019

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 5 Jun 2018

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 9 Jun 2020

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

firmai/mtss-gan The Alan Turing Institute 2020

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 15 Nov 2021

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 8 Mar 2023

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 3 Apr 2021

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 1 Nov 2021

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

jaimeperezsanchez/gan_scenario_forecasting 12 Jan 2022

This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center.