用多层感知机预测天文望远镜运行数据，实现小时级精准预报。
Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model
- 基于多变量时间序列构建神经网络，同时预测15个传感器数据。
- 在300样本（5小时）输入下，均方误差0.019，中位绝对偏差0.032。
- 模型收敛快、性能稳定，适合在线异常检测与预测性维护。
本研究提出一种正常行为模型（NBM），用于预测ASTRI-Horn切伦科夫望远镜在正常运行状态下的监测时间序列数据。分析了2022年9月至2024年7月间由望远镜控制单元采集的15个物理变量，涵盖方位与俯仰电机的传感器数据。经过数据清洗、重采样、特征选择与相关性分析后，将数据划分为固定长度的时间段，其中前I个样本作为输入序列，预测长度T表示需预测的未来时间步数。采用滑动窗口策略增加样本数量。使用多层感知机（MLP）对所有特征进行多变量联合预测。模型性能通过均方误差（MSE）和归一化中位绝对偏差（NMAD）评估，并与长短期记忆网络（LSTM）对比。MLP在不同输入-输出配置下表现一致，性能与LSTM相当但收敛更快。最优配置（4层隐藏层，每层720个单元，输入与预测均为300样本，对应1分钟分辨率下5小时）下，测试集上取得MSE 0.019±0.003、NMAD 0.032±0.009。将预测时长扩展至6.5小时（当前配置上限）未导致性能下降，验证了其在小时级预测中的有效性。该模型为在线监测中早期异常检测提供了有力工具，是未来开发故障预测与健康管理系统的基石。
原文摘要 · Abstract (English)
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.
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