PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats, gan, kalman-filter
Pytorch/Keras implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
This is the code notebook for the blog post on using Python and Auto ARIMA
Time series Forecasting of Wind speed based on different deep learning methods LSTM - GRU
Using my smart meter electricity data from Baltimore Gas and Electric to forecast my energy demand using support vector regression.
time series forecasting using keras, inlcuding LSTM,RNN,MLP,GRU,SVR and multi-lag training and forecasting method, ICONIP2017 paper.
Image Dataset Repo that has been created as a collaborative effort for PlanetEarth - Smog Classification Project
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
