  Deep learning techniques have recently shown promise in the field of anomaly
detection, providing a flexible and effective method of modelling systems in
comparison to traditional statistical modelling and signal processing-based
methods. However, there are a few well publicised issues Neural Networks (NN)s
face such as generalisation ability, requiring large volumes of labelled data
to be able to train effectively and understanding spatial context in data. This
paper introduces a novel NN architecture which hybridises the
Long-Short-Term-Memory (LSTM) and Capsule Networks into a single network in a
branched input Autoencoder architecture for use on multivariate time series
data. The proposed method uses an unsupervised learning technique to overcome
the issues with finding large volumes of labelled training data. Experimental
results show that without hyperparameter optimisation, using Capsules
significantly reduces overfitting and improves the training efficiency.
Additionally, results also show that the branched input models can learn
multivariate data more consistently with or without Capsules in comparison to
the non-branched input models. The proposed model architecture was also tested
on an open-source benchmark, where it achieved state-of-the-art performance in
outlier detection, and overall performs best over the metrics tested in
comparison to current state-of-the art methods.
