DeepantDetector
Detect anomalies in time series using deep-learning-based forecasting approach
Since R2025a
Description
The DeepantDetector object uses a deep convolutional neural
network (CNN) architecture to implement a detector model capable of
being trained to detect anomalies in time series data using only normal data.
By comparing forecasted values with measured data within a detection window, the detector identifies anomalies as significant differences between forecasted and measured observations. You can control the sensitivity of the detector by modifying a set of threshold and window-sizing properties.
Creating a DeepantDetector object is the first step in a workflow that
includes creation, training, testing, assessing, and, if necessary, modifying the detector.
For information on the workflow for developing a Predictive Maintenance Toolbox™ anomaly detector, see Detecting Anomalies in Time Series Using Deep Learning Detector Models.
This anomaly detector model was inspired by the architecture proposed in the paper in [1].
For more information on the functions this workflow uses, see Object Functions.
Creation
Create a DeepantDetector object by using the deepantAD
function.
Properties
Object Functions
train | Train anomaly detector and obtain detection threshold |
detect | Detect anomalies in time series using trained detector model |
plot | Plot detected anomalies and anomaly scores in time series |
plotHistogram | Plot histogram of anomaly scores and detection threshold |
updateDetector | Update settings of a trained anomaly detector and recompute detection threshold |
References
[1] Munir, Mohsin, Shoaib Ahmed Siddiqui, Andreas Dengel, and Sheraz Ahmed. “DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series.” IEEE Access 7 (2019): 1991–2005. https://doi.org/10.1109/ACCESS.2018.2886457.
Version History
Introduced in R2025a
