TcnDetector
Description
The TcnDetector object uses a temporal convolutional network
(TCN) architecture to implement a detector model capable of being
trained to detect anomalies in time series data using only nominal data.
By comparing reconstructed values with observed data within a detection window, the TCN detector identifies anomalies as significant deviations from expected patterns. You can control the sensitivity of the detector by modifying a set of threshold properties
Creating a TcnDetector object is the first step in a workflow that
includes training the TCN model with normal data, testing the model with anomalous data, and
validating the model by visualizing detection effectiveness on anomalous data using plotting
functions. To improve detection performance, you can change certain properties, such as
threshold properties, by updating the trained model without retraining. To change other
properties, you must create a new detector object and specify the new properties using
name-value arguments. You cannot modify detector properties using dot notation.
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 TcnDetector object by using the tcnAD
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] He, Yangdong, and Jiabao Zhao. “Temporal Convolutional Networks for Anomaly Detection in Time Series.” Journal of Physics: Conference Series, vol. 1213, no. 4, June 2019, p. 042050. DOI.org (Crossref), https://doi.org/10.1088/1742-6596/1213/4/042050.
Version History
Introduced in R2025a
