/MvTAe-Multivariate-Temporal-Autoencoder

This code demonstrates a multi-branch deep neural network approach to tackling the problem of multivariate temporal sequence prediction by modelling a latent state vector representation of data windows through the use of a recurrent autoencoder and predictive model.

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

MvTAe-Multivariate-Temporal-Autoencoder

This repository code demonstrates a multi-branch deep neural network approach to tackling the problem of multivariate temporal sequence prediction by modelling a latent state vector representation of data windows through the use of a recurrent autoencoder and predictive model.

This code is an accompaniment to the following research paper: Multivariate Temporal Autoencoder for Predictive Reconstruction of Deep Sequences

An article post about this code can also be found HERE

Multivariate Temporal Autoencoder (MvTAe) Architecture Diagram

Multivariate Temporal Autoencoder (MvTAe) Architecture Diagram