  In this paper, we introduce Masked Anomaly Detection (MAD), a general
self-supervised learning task for multivariate time series anomaly detection.
With the increasing availability of sensor data from industrial systems, being
able to detecting anomalies from streams of multivariate time series data is of
significant importance. Given the scarcity of anomalies in real-world
applications, the majority of literature has been focusing on modeling
normality. The learned normal representations can empower anomaly detection as
the model has learned to capture certain key underlying data regularities. A
typical formulation is to learn a predictive model, i.e., use a window of time
series data to predict future data values. In this paper, we propose an
alternative self-supervised learning task. By randomly masking a portion of the
inputs and training a model to estimate them using the remaining ones, MAD is
an improvement over the traditional left-to-right next step prediction (NSP)
task. Our experimental results demonstrate that MAD can achieve better anomaly
detection rates over traditional NSP approaches when using exactly the same
neural network (NN) base models, and can be modified to run as fast as NSP
models during test time on the same hardware, thus making it an ideal upgrade
for many existing NSP-based NN anomaly detection models.
