Cutoff Frequency Adjustment for FFT-Based Anomaly Detectors - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Conference Papers Year : 2024

Cutoff Frequency Adjustment for FFT-Based Anomaly Detectors

Abstract

This article presents a time series anomaly detection method based on the Fast Fourier Transform (FFT) using a high-pass filter. The proposed method aims to remove low-frequency components, such as trends and seasonality, which represent the normal behavior of the series, while preserving high-frequency components associated with anomalies. The major challenge in constructing this method lies in determining the high-pass filter's cutoff frequency without prior knowledge of the intrinsic nature of the series. In addition to the traditional approach, four new distinct approaches were explored to determine the high-pass filter's cutoff frequency, making the method adaptable to various datasets. Experimental results show the effectiveness of the method in anomaly detection using high-pass FFT filters that have a cutoff frequency adjusted by change points, outperforming traditional techniques such as statistical and machine learning methods in terms of F1 score, precision, accuracy, and execution time.
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Dates and versions

lirmm-04683135 , version 1 (01-09-2024)

Identifiers

  • HAL Id : lirmm-04683135 , version 1

Cite

Ellen Paixão Silva, Helga Balbi, Esther Pacitti, Fabio Porto, Joel A. F. dos Santos, et al.. Cutoff Frequency Adjustment for FFT-Based Anomaly Detectors. SBBD 2024 - Simpósio Brasileiro de Banco de Dados, Sociedade Brasileira de Computação (SBC), Oct 2024, Florianapolis, Brazil. pp.1-5. ⟨lirmm-04683135⟩
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