  Time series sequence prediction and modelling has proven to be a challenging
endeavor in real world datasets. Two key issues are the multi-dimensionality of
data and the interaction of independent dimensions forming a latent output
signal, as well as the representation of multi-dimensional temporal data inside
of a predictive model. This paper proposes a multi-branch deep neural network
approach to tackling the aforementioned problems by modelling a latent state
vector representation of data windows through the use of a recurrent
autoencoder branch and subsequently feeding the trained latent vector
representation into a predictor branch of the model. This model is henceforth
referred to as Multivariate Temporal Autoencoder (MvTAe). The framework in this
paper utilizes a synthetic multivariate temporal dataset which contains
dimensions that combine to create a hidden output target.
