High-pass filtering via FFT to isolate high-frequency components; anomalies are flagged where the filtered magnitude departs strongly from baseline.
Details
The spectrum is computed by FFT, a cutoff is selected from the power spectrum,
low frequencies are nulled, and the inverse FFT reconstructs a high-pass
signal. Magnitudes are summarized and thresholded using harutils().
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$simple
head(dataset)
#> serie event
#> 1 1.0000000 FALSE
#> 2 0.9689124 FALSE
#> 3 0.8775826 FALSE
#> 4 0.7316889 FALSE
#> 5 0.5403023 FALSE
#> 6 0.3153224 FALSE
# Configure FFT-based anomaly detector
model <- hanr_fft()
# Fit the model
model <- fit(model, dataset$serie)
# Run detection
detection <- detect(model, dataset$serie)
# Show detected anomalies
print(detection[(detection$event),])
#> idx event type
#> 50 50 TRUE anomaly