semvec/pypi-packages huggingface dataset:
tom-pittgoogle 0.1.11
Pitt-Google broker module for the TOM Toolkit
Requires Python: >=3.6, <4
Keywords: astronomy,alert broker

[![python](https://img.shields.io/badge/python-3.7-g.svg)]()
[![Documentation Status](https://readthedocs.org/projects/tom-pittgoogle/badge/?version=latest)](https://tom-pittgoogle.readthedocs.io/en/latest/?badge=latest)

# Pitt-Google integration with TOM Toolkit

Please read the docs at [https://tom-pittgoogle.readthedocs.io/en/latest/](https://tom-pittgoogle.readthedocs.io/en/latest/).

Contact Troy Raen with questions or for authentication access (Slack @troyraen on LSSTC or ztf-broker-ops, or email troy.raen@pitt.edu).
taug 0.1.0
Time Series Forecasting and Data Augmentation using Deep Generative Models

# 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
```python
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
```python
augmenter.fit(data, epochs=64)
```
#### Generate new time series!
A few strategy for sampling have been implemented.
##### Sampling from whol
```python
augmenter.sample(n=1000)
```
### Forecasting Guide
[todo] Forecasting guide will be here!

## Supported Augmenters
Supported models for augmentation currently are as follows:
|  Model  |           Type          |   Supported Time Series  |                                Description                                |
|:-------:|:-----------------------:|:------------------------:|:-------------------------------------------------------------------------:|
| LSTMVAE | Variational Autoencoder | Univariate, fixed length | A Variational Autoencoder with stacked LSTM layers for encoder and decoder |

## Supported Forecasters
Supported models for time series forecasting are as follows:

## Contributors
The list of the current contributors:
- Sasan Barak
- Amirabbas Asadi