Comparison of Anomaly Detection between Statistical Method and Undercomplete Autoencoder

Anomaly detection is one of the key issues in the domain of unsupervised machine learning gaining importance in diverse research and application areas. In the presented work, we aim to compare a specifically developed statistical method with a generic sparse autoencoder approach for the anomaly detection problem. To compare performance, we apply both methods to real energy consumption data taken from supermarket stores. Firstly, the raw data was analyzed and the feature vectors were constructed. Secondly, Tukey's test was implemented on the constructed feature vectors to determine the outliers. Finally as a second method, we designed a generic unsupervised undercomplete autoencoder for the detection of the outliers in order to compare in several experiments the performance of both approaches. We also provided a discussion on the computational complexity of the techniques since, it is an important issue in real application domains.