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Comparative study in anomaly diagnosis technique for time series data

In this paper, by comparing the values of the anomaly index in various anomaly diagnosis techniques on the same dataset, it is intended to help in selecting an anomaly diagnosis technique suitable for the desired task. We compare the models from the commonly used CNN or LSTM-based autoencoder anomaly diagnosis technique to its variant model DAGMM (Deep Autoencoding Gaussian Mixture model), deep learning-based SVDD (Support Vector Data Description), and recently USAD (Unsupervised Anomaly Detection) with each other. Since the anomaly index value at the time of occurrence of anomalies varies depending on the model, it is possible to consider the model selection according to the data distribution through these index values.