This study presents a Normal Behavior Model (NBM) developed to forecast monitoring time-series data from the ASTRI-Horn Cherenkov telescope under normal operating conditions. The analysis focused on 15 physical variables acquired by the Telescope Control Unit between September 2022 and July 2024, representing sensor measurements from the Azimuth and Elevation motors. After data cleaning, resampling, feature selection, and correlation analysis, the dataset was segmented into fixed-length intervals, in which the first I samples represented the input sequence provided to the model, while the forecast length, T, indicated the number of future time steps to be predicted. A sliding-window technique was then applied to increase the number of intervals. A Multi-Layer Perceptron (MLP) was trained to perform multivariate forecasting across all features simultaneously. Model performance was evaluated using the Mean Squared Error (MSE) and the Normalized Median Absolute Deviation (NMAD), and it was also benchmarked against a Long Short-Term Memory (LSTM) network. The MLP model demonstrated consistent results across different features and I-T configurations, and matched the performance of the LSTM while converging faster. It achieved an MSE of 0.019+/-0.003 and an NMAD of 0.032+/-0.009 on the test set under its best configuration (4 hidden layers, 720 units per layer, and I-T lengths of 300 samples each, corresponding to 5 hours at 1-minute resolution). Extending the forecast horizon up to 6.5 hours-the maximum allowed by this configuration-did not degrade performance, confirming the model's effectiveness in providing reliable hour-scale predictions. The proposed NBM provides a powerful tool for enabling early anomaly detection in online ASTRI-Horn monitoring time series, offering a basis for the future development of a prognostics and health management system that supports predictive maintenance.

翻译：本研究提出了一种正常行为模型（NBM），旨在预测ASTRI-Horn切伦科夫望远镜在正常运行条件下的监测时间序列数据。分析聚焦于2022年9月至2024年7月期间由望远镜控制单元采集的15个物理变量，这些变量代表了方位角和俯仰角电机的传感器测量值。经过数据清洗、重采样、特征选择和相关性分析后，数据集被分割为固定长度的区间，其中前I个样本代表提供给模型的输入序列，而预测长度T则表示待预测的未来时间步数。随后应用滑动窗口技术以增加区间数量。训练了一个多层感知机（MLP）来同时对所有特征进行多元预测。使用均方误差（MSE）和归一化中位数绝对偏差（NMAD）评估模型性能，并与长短期记忆（LSTM）网络进行基准比较。MLP模型在不同特征及I-T配置下均表现出稳定结果，其性能与LSTM相当且收敛更快。在其最佳配置下（4个隐藏层，每层720个单元，I和T长度均为300个样本，对应于1分钟分辨率下的5小时），该模型在测试集上取得了MSE为0.019+/-0.003、NMAD为0.032+/-0.009的成绩。将预测范围扩展至6.5小时（该配置允许的最大值）并未导致性能下降，证实了该模型在提供可靠小时尺度预测方面的有效性。所提出的NBM为在线ASTRI-Horn监测时间序列的早期异常检测提供了一个强大工具，并为未来开发支持预测性维护的预测与健康管理系统奠定了基础。