The RNN model (Fig. ) is a traditional VAE featuring a Gated Recurrent Unit (GRU)-based Recurrent Neural Network (RNN) as the encoder and as the decoder . The RNN model first embeds the input sequences into fixed length vectors of length 128. The embedded vectors are then passed to the GRU, with N=3 layers, that generates a hidden layer vector of length 128. The hidden layer is layer-normalized and sent to two separate linear layers, the mean layer (µ) and the log-variance layer(σ ). The linear layers act as the "bottleneck" and changes-where necessary-the GRU output shape to the appropriate latent space vector shape-either 32, 64 or 128 dimensions, depending on which Figure : Schematic of the five deep learning model architectures, highlighting their similarities and differences. The RNN features a GRU encoder, a variational latent space and a GRU decoder. The RNN-Attention inserts an attention layer after the encoder GRU and uses convolutional layers as input and output from the variational latent space. The AAE features a non-variational latent space but inserts a discriminator network at the latent space. The WAE also features a non-variational latent space but calculates the maximum mean discrepancy between gaussian noise and the encoder generated latent space. The Transformer model implements the generic architecture with a first self-attention layer then a convolutional layer into the latent space followed by deconvolution into the masked attention and the final self-attention layer.