  We propose a simple mathematical definition and new neural architecture for
finding anomalies within discrete sequence datasets. Our model comprises of a
modified LSTM autoencoder and an array of One-Class SVMs. The LSTM takes in
elements from a sequence and creates context vectors that are used to predict
the probability distribution of the following element. These context vectors
are then used to train an array of One-Class SVMs. These SVMs are used to
determine an outlier boundary in context space.We show that our method is
consistently more stable and also outperforms standard LSTM and sliding window
anomaly detection systems on two generated datasets.
