TAug :: Time Series Data Augmentation using Deep Generative Models

Related tags

Deep Learningtaug
Overview

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:

Model Type Supported Time Series Description
LSTMVAE Variational Autoencoder Univariate, fixed length A Variational Autoencoder with stacked LSTM layers for encoder and decoder based on the paper [paper citation]

Supported Forecasters

Supported models for time series forecasting are as follows:

Contributors

The list of the current contributors:

  • Sasan Barak
  • Amirabbas Asadi
OCR Streamlit App is used to extract text from images using python's easyocr, pytorch and streamlit packages

OCR-Streamlit-App OCR Streamlit App is used to extract text from images using python's easyocr, pytorch and streamlit packages OCR app gets an image a

Siva Prakash 5 Apr 05, 2022
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning This repository is the official implementation of "SHRIMP: Sparser Random Featur

Bobby Shi 0 Dec 16, 2021
Training Structured Neural Networks Through Manifold Identification and Variance Reduction

Training Structured Neural Networks Through Manifold Identification and Variance Reduction This repository is a pytorch implementation of the Regulari

0 Dec 23, 2021
Fastquant - Backtest and optimize your trading strategies with only 3 lines of code!

fastquant 🤓 Bringing backtesting to the mainstream fastquant allows you to easily backtest investment strategies with as few as 3 lines of python cod

Lorenzo Ampil 1k Dec 29, 2022
Semantic code search implementation using Tensorflow framework and the source code data from the CodeSearchNet project

Semantic Code Search Semantic code search implementation using Tensorflow framework and the source code data from the CodeSearchNet project. The model

Chen Wu 24 Nov 29, 2022
Norm-based Analysis of Transformer

Norm-based Analysis of Transformer Implementations for 2 papers introducing to analyze Transformers using vector norms: Kobayashi+'20 Attention is Not

Goro Kobayashi 52 Dec 05, 2022
[WACV21] Code for our paper: Samuel, Atzmon and Chechik, "From Generalized zero-shot learning to long-tail with class descriptors"

DRAGON: From Generalized zero-shot learning to long-tail with class descriptors Paper Project Website Video Overview DRAGON learns to correct the bias

Dvir Samuel 25 Dec 06, 2022
This is a project based on retinaface face detection, including ghostnet and mobilenetv3

English | 简体中文 RetinaFace in PyTorch Chinese detailed blog:https://zhuanlan.zhihu.com/p/379730820 Face recognition with masks is still robust---------

pogg 59 Dec 21, 2022
Course about deep learning for computer vision and graphics co-developed by YSDA and Skoltech.

Deep Vision and Graphics This repo supplements course "Deep Vision and Graphics" taught at YSDA @fall'21. The course is the successor of "Deep Learnin

Yandex School of Data Analysis 160 Jan 02, 2023
Denoising images with Fourier Ring Correlation loss

Denoising images with Fourier Ring Correlation loss The python code accompanies the working manuscript Image quality measurements and denoising using

2 Mar 12, 2022
Fast Soft Color Segmentation

Fast Soft Color Segmentation

3 Oct 29, 2022
Self-Supervised depth kalilia

Self-Supervised depth kalilia

24 Oct 15, 2022
Code for the paper "Adapting Monolingual Models: Data can be Scarce when Language Similarity is High"

Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling Adapting Monolingual Models: Data can be Scarce when Language Similarity is High

Wietse de Vries 5 Aug 02, 2021
A PyTorch implementation of the architecture of Mask RCNN

EDIT (AS OF 4th NOVEMBER 2019): This implementation has multiple errors and as of the date 4th, November 2019 is insufficient to be utilized as a reso

Sai Himal Allu 975 Dec 30, 2022
A Lightweight Experiment & Resource Monitoring Tool 📺

Lightweight Experiment & Resource Monitoring 📺 "Did I already run this experiment before? How many resources are currently available on my cluster?"

170 Dec 28, 2022
Official implementation of Rethinking Graph Neural Architecture Search from Message-passing (CVPR2021)

Rethinking Graph Neural Architecture Search from Message-passing Intro The GNAS can automatically learn better architecture with the optimal depth of

Shaofei Cai 48 Sep 30, 2022
Code for "Intra-hour Photovoltaic Generation Forecasting based on Multi-source Data and Deep Learning Methods."

pv_predict_unet-lstm Code for "Intra-hour Photovoltaic Generation Forecasting based on Multi-source Data and Deep Learning Methods." IEEE Transactions

FolkScientistInDL 8 Oct 08, 2022
Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle

TF Watcher TF Watcher is a simple to use Python package and web app which allows you to monitor 👀 your Machine Learning training or testing process o

Rishit Dagli 54 Nov 01, 2022
Supervised Contrastive Learning for Downstream Optimized Sequence Representations

SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext

Hooman Sedghamiz 18 Oct 21, 2022
Code accompanying our paper Feature Learning in Infinite-Width Neural Networks

Empirical Experiments in "Feature Learning in Infinite-width Neural Networks" This repo contains code to replicate our experiments (Word2Vec, MAML) in

Edward Hu 37 Dec 14, 2022