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harbinger (version 1.2.767)

hmu_pca: Multivariate anomaly detector using PCA

Description

Projects multivariate observations onto principal components and flags large reconstruction errors as anomalies. Based on classical PCA.

Usage

hmu_pca()

Arguments

Value

hmu_pca object.

Details

The series is standardized, PCA is computed, and data are reconstructed from principal components. The reconstruction error is summarized and thresholded.

References

  • Jolliffe IT (2002). Principal Component Analysis. Springer.

Examples

Run this code
library(daltoolbox)

# Load multivariate example data
data(examples_harbinger)

# Use a multidimensional time series
dataset <- examples_harbinger$multidimensional
head(dataset)

# Configure PCA-based anomaly detector
model <- hmu_pca()

# Fit the model (example uses first two columns)
model <- fit(model, dataset[,1:2])

# Run detection
detection <- detect(model, dataset[,1:2])

# Show detected anomalies
print(detection[(detection$event),])

# Evaluate detections
evaluation <- evaluate(model, detection$event, dataset$event)
print(evaluation$confMatrix)

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