hanr_wavelet: Anomaly detector using Wavelets

In harbinger: A Unified Time Series Event Detection Framework

Anomaly detector using Wavelets

Description

Multiresolution decomposition via wavelets; anomalies are flagged where aggregated wavelet detail coefficients indicate unusual energy.

Usage

hanr_wavelet(filter = "haar")

Arguments

filter

Character. Available wavelet filters: haar, d4, la8, bl14, c6.

Details

The series is decomposed with MODWT and detail bands are aggregated to compute a magnitude signal that is thresholded using harutils().

Value

hanr_wavelet object

References

• Mallat S (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674–693.

Examples

library(daltoolbox)

# Load anomaly example data
data(examples_anomalies)

# Use a simple example
dataset <- examples_anomalies$simple
head(dataset)

# Configure wavelet-based anomaly detector
model <- hanr_wavelet()

# Fit the model
model <- fit(model, dataset$serie)

# Run detection
detection <- detect(model, dataset$serie)

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

harbinger documentation built on Feb. 11, 2026, 9:06 a.m.