Denoising Architecture for Unsupervised Anomaly Detection in Time-Series
This work addresses anomaly detection for industries like banking and aerospace, but it is incremental as it builds on existing LSTM Autoencoder methods.
The paper tackles the challenge of unsupervised anomaly detection in time-series data by introducing a Denoising Architecture to complement LSTM Autoencoders, resulting in increased accuracy and training speed.
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.