AlexandreWarembourg

fractal

Draw fractal image by python.

awesome-pretrained-stylegan2

A collection of pre-trained StyleGAN 2 models to download

PokeGAN

GAN for generating pokemon sprites

pixel_character_generator

Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

google-images-download

Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!

intermarche

4th place solution to datafactory challenge by Intermarché.

Hierarchical-demand-forecasting

Predict M5 kaggle dataset, by LSTM and BI-LSTM and three optimal, bottom-up, top-down reconciliation approach.

intermarche

Solution to intermarche competition

seq2seqDemandForecastingRealData

m5-forecasting-accuracy

Solution to Kaggle's M5 Forecasting Accuracy Competition

mlfinlab

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Time-Series-Transformer

A data preprocessing package for time series data. Design for machine learning and deep learning.

kaggle-predict-future-sales

Kaggle's Predict Future Sales competition project (TOP 15 solution as of March 2020)

demand_forecast

fastseq

A way to use N-Beats in fastai for sequence data

Pyro-M5-Starter-Kit

Learn Pyro through the M5 forecasting competition

M5-methods

Data, Benchmarks, and methods submitted to the M5 forecasting competition

M4-methods

Data, Benchmarks, and methods submitted to the M4 forecasting competition

awesome-time-series

list of papers, code, and other resources

n-beats

Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

seasonal

Robustly estimate trend and periodicity in a timeseries.

pm-prophet

GAM timeseries modeling with auto-changepoint detection. Inspired by Facebook Prophet and implemented in PyMC3

Kaggle-Competition-Favorita

5th place solution for Kaggle competition Favorita Grocery Sales Forecasting

cFavorita

A project for solving demand forecast of a medium retailer using a simple Deep Learning model

ESRNN-GPU

PyTorch GPU implementation of the ES-RNN model for time series forecasting

web-traffic-forecasting

Kaggle | Web Traffic Forecasting 📈

skits

scikit-learn-inspired time series

TimeSeries-Seq2Seq-deepLSTMs-Keras

This project aims to give you an introduction to how Seq2Seq based encoder-decoder neural network architectures can be applied on time series data to make forecasts. The code is implemented in pyhton with Keras (Tensorflow backend).