  Anomalies in time-series provide insights of critical scenarios across a
range of industries, from banking and aerospace to information technology,
security, and medicine. However, identifying anomalies in time-series data is
particularly challenging due to the imprecise definition of anomalies, the
frequent absence of labels, and the enormously complex temporal correlations
present in such data. The LSTM Autoencoder is an Encoder-Decoder scheme for
Anomaly Detection based on Long Short Term Memory Networks that learns to
reconstruct time-series behavior and then uses reconstruction error to identify
abnormalities. We introduce the Denoising Architecture as a complement to this
LSTM Encoder-Decoder model and investigate its effect on real-world as well as
artificially generated datasets. We demonstrate that the proposed architecture
increases both the accuracy and the training speed, thereby, making the LSTM
Autoencoder more efficient for unsupervised anomaly detection tasks.
