## 1 Introduction  
The National Center for Research and Development in Information and Telematic Technologies of the National Institute for Nuclear Physics (INFN-CNAF) represents one of the most advanced computing infrastructures in Italy and internationally, playing a key role in supporting high-intensity scientific research. Since 2003, it has hosted the Italian Tier-1, one of the core components of the Worldwide LHC Computing Grid (WLCG) ([[https://home.cern/science/computing/grid](https://home.cern/science/computing/grid)]([https://home.cern/science/computing/grid](https://home.cern/science/computing/grid)), accessed on 2 January 2025), a distributed infrastructure that manages and analyzes vast amounts of data produced by experiments at the Large Hadron Collider (LHC) ([[https://home.cern/science/accelerators/large-hadron-collider](https://home.cern/science/accelerators/large-hadron-collider)]([https://home.cern/science/accelerators/large-hadron-collider](https://home.cern/science/accelerators/large-hadron-collider)), accessed on 2 January 2025) at CERN. In addition to supporting the LHC, which uses approximately 70% of CNAF's resources, the center provides essential services for many other world-class research projects. These include astroparticle experiments, such as AMS and DarkSide, neutrino studies like ICARUS ([[https://icarus.fnal.gov](https://icarus.fnal.gov)]([https://icarus.fnal.gov](https://icarus.fnal.gov)), accessed on 2 January 2025) and JUNO ([[https://w3.lnf.infn.it/ricerca/fisica-delle-astroparticle/juno/](https://w3.lnf.infn.it/ricerca/fisica-delle-astroparticle/juno/)]([https://w3.lnf.infn.it/ricerca/fisica-delle-astroparticle/juno/](https://w3.lnf.infn.it/ricerca/fisica-delle-astroparticle/juno/)), accessed on 2 January 2025), as well as projects such as Virgo, dedicated to the detection of gravitational waves. The center is also deeply involved in the management and development of Grid Computing technologies, which allow computational resources to be distributed on a global scale. Through the integration of distributed storage systems and technologies such as IBM Spectrum Scale (former General Parallel File System--GPFS) and IBM Spectrum Protect (former Tivoli Storage Manager--TSM), CNAF ensures maximum reliability in data management and long-term storage. These systems are supported by advanced monitoring technologies based on Grafana and Sensu, which provide real-time performance and security monitoring of IT infrastructures, enabling rapid detection of any anomalies and maintaining high levels of operability. In the context of time series analysis, anomaly prediction aims to forecast the occurrence of future data points, segments, or sequences that are likely to diverge significantly from expected behavior or established patterns within the temporal dynamics of the system [1; 2]. Unlike anomaly detection [3; 4; 5], which identifies deviations retrospectively, anomaly prediction [6] proactively identifies conditions or trends that indicate potential anomalies. These predicted deviations are characterized by their rarity and unexpected nature when compared to historical trends or statistical properties of the time series. Leveraging techniques such as time series forecasting models [7; 8], recurrent neural networks [9; 10; 11; 12], Bayesian methods [13; 14], or Reinforcement Learning [15; 16], anomaly prediction enables IT systems to anticipate disruptions and take preventive measures, making it a valuable component in many different domains. Applications of anomaly prediction span a diverse range of fields, including geological hazard forecasting [17], maintenance of mechanical devices [18], and monitoring complex systems and networks. Notable examples include Power Distribution Systems [19], Integrated Energy Systems [20], and wireless sensor networks [21]. In complex distributed environments [22], anomalies can lead to severe consequences, such as performance degradation, service interruptions, or, in the worst cases, loss of critical data.