Frequency-Informed Encoder-Decoder Models with Expressivity for Time Series Anomaly Detection

Improving anomaly detection accuracy in time series data is essential for the operation of communication networks. Encoder-decoder models, which learn the correlations within the data, have been widely used for anomaly detection because the models can be trained without anomalous data. To capture the correlations within the data, encoder-decoder models are trained to reconstruct the data by using their encoders and decoders. However, existing encoder-decoder models struggle to reconstruct the input data depending on the geometric features of the data. In this paper, we focus on periodic time series data with complex geometric features and propose an expressive encoder-decoder model with learnable parameters that can exactly reconstruct periodic time series data. In anomaly detection experiments on two periodic time series datasets, our proposed model achieved higher area under the curve (AUC) scores than existing encoder-decoder models.