usAD
Create anomaly detector model that uses unsupervised dual-encoder network to detect anomalies in time series
Since R2025a
Description
Add-On Required: This feature requires the Time Series Anomaly Detection for MATLAB add-on.
detector = usAD(numChannels)UsadDetector
model with numChannels channels for each time series input to the
detector.
After you create the detector model, you can train, test, and modify it to obtain the level of performance you require. For more information about the anomaly detector workflow, see Detecting Anomalies in Time Series.
This feature requires Deep Learning Toolbox™.
detector = usAD( sets
additional options using one or more name-value arguments.numChannels,Name=Value)
For example, detector = usAD(3,Alpha=0.8,Beta=0.2) creates a detector
model for data containing three input channels and sets the Alpha and
Beta sensitivity values to 0.8 and
0.2, respectively.
Examples
Load the file sineWaveAnomalyData.mat, which contains two sets of synthetic three-channel sinusoidal signals.
sineWaveNormal contains 10 sinusoids of stable frequency and amplitude. Each signal has a series of small-amplitude impact-like imperfections. The signals have different lengths and initial phases.
load sineWaveAnomalyData.mat sineWaveNormal sineWaveAbnormal s1 = 3;
Plot input signals
Plot the first three normal signals. Each signal contains three input channels.
tiledlayout("vertical") ax = zeros(s1,1); for kj = 1:s1 ax(kj) = nexttile; plot(sineWaveNormal{kj}) title("Normal Signal Channels") end
sineWaveAbnormal contains three signals, all of the same length. Each signal in the set has one or more anomalies.
All channels of the first signal have an abrupt change in frequency that lasts for a finite time.
The second signal has a finite-duration amplitude change in one of its channels.
The third signal has spikes at random times in all channels.
Plot the three signals with anomalies.
tiledlayout("vertical") ax = zeros(s1,1); for kj = 1:s1 ax(kj) = nexttile; plot(sineWaveAbnormal{kj}) title("Anomalous Signal") end
Create Detector
Use the usAD function to create a usadDetector object with default options.
detector_usad = usAD(3)
detector_usad =
UsadDetector with properties:
ObservationWindowLength: 24
DetectionWindowLength: 24
Alpha: 0.7000
Beta: 0.3000
TrainingStride: 24
OutputSize: [128 64 32]
IsTrained: 0
NumChannels: 3
Layers: {[7×1 nnet.cnn.layer.Layer] [7×1 nnet.cnn.layer.Layer] [7×1 nnet.cnn.layer.Layer]}
Dlnet: {[1×1 dlnetwork] [1×1 dlnetwork] [1×1 dlnetwork]}
Threshold: []
ThresholdMethod: "kSigma"
ThresholdParameter: 3
ThresholdFunction: []
Normalization: "zscore"
DetectionStride: 24
Train Detector
Train detector_usad using the normal data. Specify MaxEpochs as a name-value pair.
detector_usad = train(detector_usad,sineWaveNormal,MaxEpochs=100);
|======================================================================================| | Iteration | Epoch | Time Elapsed | Base Learning | AE1 Training | AE2 Training | | | | (hh:mm:ss) | Rate | Loss | Loss | |======================================================================================| | 1 | 1 | 00:00:03 | 0.0010 | 1.2215 | 1.2113 | | 50 | 13 | 00:00:10 | 0.0010 | 1.2639 | -1.1104 | | 100 | 25 | 00:00:17 | 0.0010 | 1.5804 | -1.4967 | | 150 | 38 | 00:00:23 | 0.0010 | 2.0047 | -1.9536 | | 200 | 50 | 00:00:29 | 0.0010 | 1.8222 | -1.7869 | | 250 | 63 | 00:00:35 | 0.0010 | 2.0199 | -1.9919 | | 300 | 75 | 00:00:40 | 0.0010 | 1.8274 | -1.8075 | | 350 | 88 | 00:00:47 | 0.0010 | 2.0227 | -2.0050 | | 400 | 100 | 00:00:53 | 0.0010 | 1.8292 | -1.8160 | |=====================================================================| Computing threshold... Threshold computation completed.
View the threshold that train computes and saves within detector_usad. This computed value is influenced by random factors, such as which subsets of the data are used for training, and can change somewhat for different training sessions and different machines.
thresh = detector_usad.Threshold
thresh = 2.8269
Plot the histogram of the anomaly scores for the normal data. Each score is calculated over a single detection window. The threshold, plotted as a vertical line, does not always completely bound the scores.
plotHistogram(detector_usad,sineWaveNormal)
Use Detector to Identify Anomalies
Use the detect function to determine the anomaly scores for the anomalous data.
results = detect(detector_usad, sineWaveAbnormal)
results=3×1 cell array
54×3 table
54×3 table
54×3 table
results is a cell array that contains three tables, one table for each channel. Each cell table contains three variables: WindowLabel, WindowAnomalyScore, and WindowStartIndices. Confirm the table variable names.
varnames = results{1}.Properties.VariableNamesvarnames = 1×3 cell array
"'Labels'" "'AnomalyScores'" "'StartIndices'"
Plot Results
Plot a histogram that shows the normal data, the anomalous data, and the threshold in one plot.
plotHistogram(detector_usad,sineWaveNormal,sineWaveAbnormal)
The histogram uses different colors for the normal and anomalous data.
Plot the detected anomalies of the third abnormal signal set.
plot(detector_usad,sineWaveAbnormal{3})
The top plot shows an overlay of red where the anomalies occur. The bottom plot shows how effective the threshold is at dividing the normal from the abnormal scores for Signal set 3.
Input Arguments
Name-Value Arguments
Output Arguments
Version History
Introduced in R2025aSee Also
UsadDetector | train | detect | plot | plotHistogram | updateDetector
