Anomaly detector using Wavelets

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.

Value

hanr_wavelet object

Details

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

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)
#>       serie event
#> 1 1.0000000 FALSE
#> 2 0.9689124 FALSE
#> 3 0.8775826 FALSE
#> 4 0.7316889 FALSE
#> 5 0.5403023 FALSE
#> 6 0.3153224 FALSE

# 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),])
#>    idx event    type
#> 50  50  TRUE anomaly