DeepAI AI Chat
Log In Sign Up

A Spectral Enabled GAN for Time Series Data Generation

by   Kaleb E Smith, et al.

Time dependent data is a main source of information in today's data driven world. Generating this type of data though has shown its challenges and made it an interesting research area in the field of generative machine learning. One such approach was that by Smith et al. who developed Time Series Generative Adversarial Network (TSGAN) which showed promising performance in generating time dependent data and the ability of few shot generation though being flawed in certain aspects of training and learning. This paper looks to improve on the results from TSGAN and address those flaws by unifying the training of the independent networks in TSGAN and creating a dependency both in training and learning. This improvement, called unified TSGAN (uTSGAN) was tested and comapred both quantitatively and qualitatively to its predecessor on 70 benchmark time series data sets used in the community. uTSGAN showed to outperform TSGAN in 80% of the data sets by the same number of training epochs and 60% of the data sets in 3/4th the amount of training time or less while maintaining the few shot generation ability with better FID scores across those data sets.


page 9

page 10

page 12

page 13

page 14

page 15

page 16

page 17


Conditional GAN for timeseries generation

It is abundantly clear that time dependent data is a vital source of inf...

ECG synthesis with Neural ODE and GAN models

Continuous medical time series data such as ECG is one of the most compl...

Benchmark time series data sets for PyTorch – the torchtime package

The development of models for Electronic Health Record data is an area o...

An analysis of deep neural networks for predicting trends in time series data

Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was propo...

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data

There is a recent growing interest in applying Deep Learning techniques ...

Visual Evaluation of Generative Adversarial Networks for Time Series Data

A crucial factor to trust Machine Learning (ML) algorithm decisions is a...

DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data

Gravitational lensing is the relativistic effect generated by massive bo...