IvanBongiorni/GAN-RNN_Timeseries-imputation
Recurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.
This project helps data scientists fill in missing values within time-series datasets, ensuring a complete and accurate foundation for analysis or forecasting. It takes your raw time-series data with gaps and outputs a cleaned version where those gaps have been intelligently estimated and filled. Data scientists working with sequential data, such as sensor readings, financial logs, or website traffic, would use this tool.
174 stars. No commits in the last 6 months.
Use this if you need to reliably impute missing data points in your time series, especially when traditional methods fall short or you're dealing with complex patterns.
Not ideal if your dataset is not time-series based or if you only have a few isolated missing values that can be handled with simpler statistical methods.
Stars
174
Forks
27
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 29, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/IvanBongiorni/GAN-RNN_Timeseries-imputation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
huggingface/pytorch-pretrained-BigGAN
🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
torchgan/torchgan
Research Framework for easy and efficient training of GANs based on Pytorch
metal3d/keras-video-generators
Keras generators to generate sequences from videos as input
GANs-in-Action/gans-in-action
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
junyanz/pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch