time-series-9

Time Series Generation

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

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

Stephanie L. Hyland, Cristóbal Esteban, G. Rätsch•Wed Jun 07 2017

This work proposes a Recurrent GAN (RGAN) and Recurrent Conditional GGAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data.

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GRATIS: GeneRAting TIme Series with diverse and controllable characteristics

Yanfei Kang, Rob J Hyndman, Feng Li•Wed Mar 06 2019

This work proposes GeneRAting TIme Series with diverse and controllable characteristics, named GRATIS, with the use of mixture autoregressive (MAR) models, and demonstrates the usefulness of the time series generation process through a time series forecasting application.

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EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals

R. Schirrmeister, T. Ball, K. Hartmann•Mon Jun 04 2018

Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserstein GANs to stabilize training and investigate a range of architectural choices critical for time series generation (most notably up- and down-sampling). For evaluation we consider and compare different metrics such as Inception score, Frechet inception distance and sliced Wasserstein distance, together showing that our EEG-GAN framework generated naturalistic EEG examples. It thus opens up a range of new generative application scenarios in the neuroscientific and neurological context, such as data augmentation in brain-computer interfacing tasks, EEG super-sampling, or restoration of corrupted data segments. The possibility to generate signals of a certain class and/or with specific properties may also open a new avenue for research into the underlying structure of brain signals.

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Conditional Sig-Wasserstein GANs for Time Series Generation

Hao Ni, Lukasz Szpruch, Magnus Wiese, Shujian Liao, Baoren Xiao•Mon Jun 08 2020

A novel generator, called the conditional AR-FNN, is developed, designed to capture the temporal dependence of time series and can be efficiently trained, and consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.

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TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Ian Beaver, Cynthia Freeman, Abhyuday Desai, Zuhui Wang•Sun Nov 14 2021

This work proposes a novel architecture for synthetically generating time-series data with the use of Variational Auto-Encoders (VAEs) that can incorporate domain-specific time-patterns such as polynomial trends and seasonalities to provide interpretable outputs.

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Vector Quantized Time Series Generation with a Bidirectional Prior Model

Daesoo Lee, Sara Malacarne, Erlend Aune•Tue Mar 07 2023

TimeVQVAE is proposed, the first work, to the knowledge, that uses vector quantization (VQ) techniques to address the TSG problem, and the priors of the discrete latent spaces are learned with bidirectional transformer models that can better capture global temporal consistency.

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Monte Carlo simulation of SDEs using GANs

Jorino van Rhijn, C. Oosterlee, L. Grzelak, Shuaiqiang Liu•Fri Apr 02 2021

It is demonstrated how standard GANs may give rise to non-parsimonious input-output maps that are sensitive to perturbations, which motivates the need for constraints and regularisation on GAN generators.

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Sig-wasserstein GANs for time series generation

Hao Ni, Lukasz Szpruch, Magnus Wiese, Shujian Liao, Baoren Xiao, Marc Sabaté-Vidales•Sun Oct 31 2021

The SigWGAN is developed by combining continuous-time stochastic models with the newly proposed signature W1 metric, which allows turning computationally challenging GAN min-max problem into supervised learning while generating high fidelity samples.

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Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks

Jaime P'erez, Patricia Arroba, Jose M. Moya•Tue Jan 11 2022

This work combines the disciplines of GAN-based data augmentation and scenario forecasting, filling the gap in the generation of synthetic data in DCs, and proposes a methodology to increase the variability and heterogeneity of the generated data by introducing on-demand anomalies without additional effort or expert knowledge.

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