hanr_fft_amoc: Anomaly Detector using FFT with AMOC Cutoff

In harbinger: A Unified Time Series Event Detection Framework

Anomaly Detector using FFT with AMOC Cutoff

Description

This detector combines FFT-based spectral filtering with an AMOC change-point cutoff on the power spectrum. Frequencies below the selected cutoff are removed, the signal is reconstructed from the remaining high-frequency content, and the residual is scored for anomalies.

This function extends the HARBINGER framework and returns an object of class hanr_fft_amoc.

Usage

hanr_fft_amoc()

Value

hanr_fft_amoc object

References

• Sobrinho, E. P., Souza, J., Lima, J., Giusti, L., Bezerra, E., Coutinho, R., Baroni, L., Pacitti, E., Porto, F., Belloze, K., Ogasawara, E. Fine-Tuning Detection Criteria for Enhancing Anomaly Detection in Time Series. In: Simpósio Brasileiro de Banco de Dados (SBBD). SBC, 29 Sep. 2025. doi:10.5753/sbbd.2025.247063

Examples

library(daltoolbox)

# Load anomaly example data
data(examples_anomalies)

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

# Configure FFT+AMOC detector
model <- hanr_fft_amoc()

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

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

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

harbinger documentation built on May 14, 2026, 5:06 p.m.