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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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#	Name
1	Ellen Silva(ellen.paixao@aluno.cefet-rj.br)
2	Helga Balbi(helga.balbi@cefet-rj.br)
3	Esther Pacitti( Esther.Pacitti@inria.fr)
4	Fabio Porto (fporto@lncc.br)
5	Joel Santos(joel.santos@cefet-rj.br)
6	Eduardo Ogasawara(eogasawara@ieee.org)

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