TAug :: Time Series Data Augmentation using Deep Generative Models

Note!!! The package is under development so be careful for using in production!

Features

• Time Series Data Augmentation using Deep Generative Models
• Visualizing the Latent Space of Generative Models
• Time Series Forecasting using Deep Neural Networks

Installation

You can install the last stable version using pip

pip install taug

How to Use

Augmentation Guide

Create an augmenter

from taug.augmenters.vae import LSTMVAE
from taug.augmenters.vae import VAEAugmenter

# create a variational autoencoder
vae = LSTMVAE(series_len=100)
# use the created vae as an augmenter
augmenter = VAEAugmenter(vae)

The above code uses the default settings for the LSTM-VAE model. You can customize its architecture or use your own model for encoder and decoder. Note currently we only support Keras models.

Train the augmenter

augmenter.fit(data, epochs=64)

Generate new time series!

Two strategies for sampling have been implemented.
You can simply sample from the latent space. Here n is the number of generated series

augmenter.sample(n=1000)

You also can generate time series by reconstructing a set of series.

augmenter.sample(X=data)

In latter case you can control the variety of generated time series using sigma

augmenter.sample(X=data, sigma=0.2)

Forecasting Guide

[todo] Forecasting guide will be here!

Supported Augmenters

Supported models for augmentation currently are as follows:

Supported Forecasters

Supported models for time series forecasting are as follows:

Contributors

The list of the current contributors:

• Sasan Barak
• Amirabbas Asadi