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Multivariate Temporal Autoencoder for Predictive Reconstruction of Deep Sequences
[article]
2020
arXiv
pre-print
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
arXiv:2010.03661v1
fatcat:leuop2djdbdd3pkr4ff62eactq