hanr_fft_sma: Anomaly Detector using Adaptive FFT and Moving Average

In harbinger: A Unified Time Series Event Detection Framework

Anomaly Detector using Adaptive FFT and Moving Average

Description

This detector combines FFT-based spectral analysis with an adaptive moving-average filter. The residual signal is scored for anomalies, and a grouping strategy reduces false positives by keeping a representative point from each cluster.

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

Usage

hanr_fft_sma()

Value

hanr_fft_sma 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)

#Using simple example
dataset <- examples_anomalies$simple
head(dataset)

# setting up time series fft detector
model <- hanr_fft_sma()

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

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

# filtering detected events
print(detection[(detection$event),])

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