  In this paper, we compare different types of Recurrent Neural Network (RNN)
Encoder-Decoders in anomaly detection viewpoint. We focused on finding the
model that can learn the same data more effectively. We compared multiple
models under the same conditions, such as the number of parameters, optimizer,
and learning rate. However, the difference is whether to predict the future
sequence or restore the current sequence. We constructed the dataset with
simple vectors and used them for the experiment. Finally, we experimentally
confirmed that the model performs better when the model restores the current
sequence, rather than predict the future sequence.
