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Anomaly Detector using Adaptive FFT and Moving Average

Source: R/hanr_fft_sma.R
hanr_fft_sma.Rd

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)
#>       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

# 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)
#> Warning: longer object length is not a multiple of shorter object length

# filtering detected events
print(detection[(detection$event),])
#>    idx event    type
#> 50  50  TRUE anomaly