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Abstract Fast and accurate anomaly detection is critical in telemetry systems because it helps operators take appropriate actions in response to abnormal behaviours. However, recent techniques are accurate but not fast enough to deal with real-time data. There is a need to reduce the anomaly detection time, which motivates us to propose two new algorithms called AnDePeD (Anomaly Detector on Periodic Data) and AnDePed Pro. The novelty of the proposed algorithms lies in exploiting the periodic nature of data in anomaly detection. Our proposed algorithms apply a variational mode decomposition technique to find and extract periodic components from the original data before using Long Short-Term Memory neural networks to detect anomalies in the remainder time series. Furthermore, our methods include advanced techniques to eliminate prediction errors and automatically tune operational parameters. Extensive numerical results show that the proposed algorithms achieve comparable performance in terms of Precision, Recall, F-score, and MCC metrics while outperforming most of the state-of-the-art anomaly detection approaches in terms of initialisation delay and detection delay, which is favourable for practical applications.

In recent years, convolutional neural networks (CNNs) have emerged as highly efficient architectures for image and audio classification tasks, gaining widespread adoption in state-of-the-art methodologies. While CNNs excel in machine learning scenarios where the data representation exhibits a grid structure, they face challenges in generalizing to other data types. For instance, they struggle with data structured on 3D meshes (e.g., measurements from a network of meteorological stations) or data represented by graph structures (e.g., molecular graphs). To address such challenges, the scientific literature proposes novel graph-based convolutional network architectures, extending the classical convolution concept to data structures defined by graphs. In this paper, we use such a deep learning architecture to examine graphs defined using the ICD-10 codes appearing in the medical data of patients who suffered hemorrhagic stroke in Hungary in the period 2006–2012. The purpose of the analysis is to predict the risk of stroke by examining a patient’s ICD graph. Finally, we also compare the effectiveness of this method with classical machine learning classification methods. The results demonstrate that the graph-based method can predict the risk of stroke with an accuracy of over 73%, which is more than 10% higher than the classical methods.