## 2 Related Works  
This section explores methods and techniques for predicting anomalies in time series data evaluated at regular intervals, in settings similar to the one considered in this work, focusing on both statistical and machine learning methods.  
[PERSON] et al. [33] propose a hybrid anomaly detection approach using Long Short-Term Memory (LSTM)-Autoencoder architecture. The LSTM encoder compresses the time series into a latent representation, while the decoder reconstructs it, with anomalies identified when reconstruction errors exceed a threshold. Similar approaches are discussed in works by [PERSON] and [PERSON] [34] and [PERSON] et al. [35]. [PERSON] et al. [36] extend this idea by combining LSTM-Autoencoders with Kernel Density Estimation (KDE), which avoids fixed thresholds by using probabilistic density estimates for anomaly detection.  
Statistical methods such as KDE are effective in estimating data distributions without predefined assumptions, labeling low-density points as anomalies [37]. However, these approaches often fail to capture temporal dependencies in sequential data. Hybrid methods, like those by [PERSON] et al., address this limitation by integrating statistical and machine learning techniques.  
Isolation Forest, introduced by [PERSON] et al. [38], isolates anomalies based on shorter paths in randomly generated trees, leveraging the distinctiveness of anomalies. This method has been applied to contexts like smart aquaculture [39] and has been further developed by [PERSON] et al. [40], who introduced an approach that integrates the use of randomly initialized neural networks and an innovative method for anomaly score calculation, and by [PERSON] et al. [41], who introduced a dynamic and adaptive management of the contamination parameter. Clustering methods, such as K-Means, also contribute to anomaly detection by identifying data points distant from cluster centroids, as demonstrated by [PERSON] et al. [42] and [PERSON] et al. [43].  
Supervised learning techniques leverage labeled datasets to train models for distinguishing between normal and anomalous patterns, using architectures like LSTMs, Random Forests, and one-class SVMs. Recently, Graph Neural Networks (GNNs) have emerged as a powerful tool for anomaly detection, particularly in contexts requiring structured data analysis. The Dynamic Graph Neural Network (DGNN) proposed by [PERSON] et al. [44] models the relationships between time series metrics, introducing subgraphs and adaptive attention mechanisms for efficient anomaly detection. Other works, such as those by [PERSON] et al. [45, 46], further explore GNN applications in anomaly detection.  
Finally, comparative studies by [PERSON] et al. [47] and [PERSON] et al. [48] assess the effectiveness and robustness of these methods, providing insights into their suitability for different anomaly detection scenarios.  
In our work, we utilized some of the previously established techniques, specifically LSTM-Autoencoders, during the preliminary phase to identify potential anomalies. However, the predictive models for these anomalies discussed in this article are based on traditional architectures designed for analyzing temporal sequences.