Metadata-Version: 2.1
Name: taug
Version: 0.1.0
Summary: Time Series Forecasting and Data Augmentation using Deep Generative Models 
Home-page: https://github.com/amirabbasasadi/taug
Author: Amirabbas Asadi
Author-email: amir137825@gmail.com
License: UNKNOWN
Description: # 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
Platform: UNKNOWN
Description-Content-Type: text/markdown
