Denoising Architecture for Unsupervised Anomaly Detection in Time-Series

TL;DR

This paper proposes a denoising architecture to enhance LSTM autoencoders for unsupervised anomaly detection in time-series data, improving accuracy and training speed across real-world and synthetic datasets.

Contribution

The introduction of a denoising architecture as a complement to LSTM autoencoders offers a novel approach to improve anomaly detection performance.

Findings

Increased detection accuracy on various datasets

Faster training convergence

Enhanced robustness to noisy data

Abstract

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.