Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.19984v1</id>\n    <title>Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model</title>\n    <updated>2026-02-23T15:51:50Z</updated>\n    <link href='https://arxiv.org/abs/2602.19984v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.19984v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='astro-ph.IM'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='astro-ph.HE'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n    <published>2026-02-23T15:51:50Z</published>\n    <arxiv:comment>15 pages, 12 figures</arxiv:comment>\n    <arxiv:primary_category term='astro-ph.IM'/>\n    <arxiv:journal_ref>Astronomy and Computing 55 (2026) 101071</arxiv:journal_ref>\n    <author>\n      <name>Federico Incardona</name>\n    </author>\n    <author>\n      <name>Alessandro Costa</name>\n    </author>\n    <author>\n      <name>Farida Farsian</name>\n    </author>\n    <author>\n      <name>Francesco Franchina</name>\n    </author>\n    <author>\n      <name>Giuseppe Leto</name>\n    </author>\n    <author>\n      <name>Emilio Mastriani</name>\n    </author>\n    <author>\n      <name>Kevin Munari</name>\n    </author>\n    <author>\n      <name>Giovanni Pareschi</name>\n    </author>\n    <author>\n      <name>Salvatore Scuderi</name>\n    </author>\n    <author>\n      <name>Sebastiano Spinello</name>\n    </author>\n    <author>\n      <name>Gino Tosti</name>\n    </author>\n    <arxiv:doi>10.1016/j.ascom.2026.101071</arxiv:doi>\n    <link href='https://doi.org/10.1016/j.ascom.2026.101071' rel='related' title='doi'/>\n  </entry>"
}