A Comparative Analysis of Anomaly Detection Techniques in Cellular Data

In this paper we compare various anomaly detection techniques for cellular network data using a multivariate dataset from Milan and Trentino. We evaluate traditional statistical methods (Z-score, IQR), machine learning (One-Class SVM), and deep learning approaches (LSTM-Autoencoder, Transformer). Experimental results indicate that deep learning models significantly outperform traditional methods in terms of both accuracy and efficiency. The Transformer model achieved 96.5% accuracy in 23 epochs, compared to 93% by the LSTM-Autoencoder in 40 epochs and 86% by the One-Class SVM.