Abstract
Given a large-scale rhythmic time series containing mostly normal data segments (or ‘beats’), can
we learn how to detect anomalous beats in an effective yet efficient way? For example, how can
we detect anomalous beats from electrocardiogram
(ECG) readings? Existing approaches either require excessively high amounts of labeled and balanced data for classification, or rely on less regularized reconstructions, resulting in lower accuracy in anomaly detection. Therefore, we propose
BeatGAN, an unsupervised anomaly detection algorithm for time series data. BeatGAN outputs
explainable results to pinpoint the anomalous time
ticks of an input beat, by comparing them to adversarially generated beats. Its robustness is guaranteed by its regularization of reconstruction error
using an adversarial generation approach, as well
as data augmentation using time series warping.
Experiments show that BeatGAN accurately and
efficiently detects anomalous beats in ECG time
series, and routes doctors’ attention to anomalous
time ticks, achieving accuracy of nearly 0.95 AUC,
and very fast inference (2.6 ms per beat). In addition, we show that BeatGAN accurately detects
unusual motions from multivariate motion-capture
time series data, illustrating its generality