Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

Pytorch implementation of the paper "Time-series Generative Adversarial Networks".

This repository is a non-official implementation of TimeGAN (Yoon et al., NIPS2019) using PyTorch.

A pytorch implementation of Time-series Generative Adversarial Networks (https://github.com/jsyoon0823/TimeGAN)

TensorFlow implementation of TimeGAN model for synthetic time series generation with generative adversarial networks.

Use pytorch to rebulid TimeGAN.

PyTorch implementations of FinGAN and TimeGAN to generate financial time series

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MC-TimeGAN – a multivariate conditional Time-Series GAN framework for synthesizing distribution-grid congestion data.

Multivariate Time Series Imputation with Generative Adversarial Networks

An comparative investigation into WGAN-GP, CTGAN, TimeGAN and DoppelGANger usage for generating synthetic time series finance data for use in forecasting model

TimeGANs for synthetic data generation in cyber-physical systems using Tensorflow

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TensorFlow 2.X implementation of TimeGAN

The SWAN-SF dataset is now fully preprocessed, optimized, and ready for binary classification tasks. Our team is excited to release the enhanced version of the SWAN-SF dataset across all five partitions.

TCN TimeGAN Project

Create a TimeGAN to generate synthetic stock data

Julia implementation of "Time-series Generative Adversarial Networks (TimeGAN)"

Time Series Generative Adversarial Network implementation in PyTorch

Deep Learning Methods for Time Series Data Mining (Time series Classification, Augmentation, TimeGAN...) implemented by torch

Our primary objective is to create complex, multi-dimensional stock return data from sampled Gaussian noise. The project utilizes 1-D convolutional neural networks (CNNs) within the GAN architecture for both the generator and discriminator components.

TimeGAN Architecture and Training with Tensorflow, learning the data generation process across features and time, combining adversarial and supervised training with time-series embedding, the four components of the TimeGAN architecture, joint training of autoencoder and adversarial network, training in three phases

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基于GAN的时间序列生成

Unofficial implementation of TimeGAN (Yoon et al., NIPS 2019) in PyTorch 2.

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Implementation of Time-series Generative Adversarial Networks(TimeGAN, https://github.com/jsyoon0823/TimeGAN) with using keras

A simple generative adversarial network for trajectory generation.

Time-series Generative Adversarial Networks applied to financial time-series

My implementation of TimeGAN using PyTorch. I applied it to generate synthetic ECGs, and evaluated the signals obtained with data utility and privacy preservation metrics.