Time Series Anomaly Detection Using Signal Processing and Deep Learning

In this paper, we propose a two-step approach for time series anomaly detection that combines signal processing techniques with deep learning methods. In the first step, we apply a bandpass filter to the time series data to reduce noise and highlight relevant frequency components, which enhances the signals in them. In the second step, we utilize a Functional Neural Network Autoencoder for anomaly detection, leveraging its ability to capture non-linear temporal relationships in the data. By learning a compact latent representation and remapping the filtered time series, the Autoencoder effectively identifies deviations from normal patterns, allowing us to detect anomalies. Our experiments on several benchmark datasets demonstrate that bandpass filtering consistently improves the performance of deep learning methods, including the Functional Neural Network Autoencoder, by refining the input data. Our proposed approach achieves superior performance of up to 20% in detecting anomalies, particularly in a time series with intricate structures, highlighting its potential for practical applications in multiple domains.