Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model

• URL: http://arxiv.org/abs/2602.19984v1
• Date: Mon, 23 Feb 2026 15:51:50 GMT
• Title: Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model
• Authors: Federico Incardona, Alessandro Costa, Farida Farsian, Francesco Franchina, Giuseppe Leto, Emilio Mastriani, Kevin Munari, Giovanni Pareschi, Salvatore Scuderi, Sebastiano Spinello, Gino Tosti,
• Abstract summary: The analysis focused on 15 physical variables acquired by the Telescope Control Unit between September 2022 and July 2024.<n>The model demonstrated consistent results across different features and I-T configurations.<n>The proposed NBM provides a powerful tool for enabling early anomaly detection in online ASTRI-Horn monitoring time series.
• Score: 29.749836788447226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling [51.78972657142583]
  We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of 11.5K.<n>To overcome the scalability bottleneck in existing pre-trained time series foundation models, we perform Serial Scaling in three dimensions.
  arXiv  Detail & Related papers  (2026-03-05T04:13:57Z)
• Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking [51.56484100374058]
  We evaluate whether a fully automatic, purely feedback-driven ESN can serve as a competitive alternative to widely used statistical forecasting methods.<n>Forecast accuracy is measured using MASE and sMAPE and benchmarked against simple benchmarks like drift and seasonal naive and statistical models.
  arXiv  Detail & Related papers  (2026-02-03T16:01:22Z)
• LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G [85.58816960936069]
  Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Time (Near-RT) latency and computational constraints.<n>This paper investigates a post-Transformer paradigm for efficient radio telemetry forecasting.<n>We propose a quantum-inspired state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels.
  arXiv  Detail & Related papers  (2026-01-18T12:08:38Z)
• Unsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series [1.0874100424278175]
  anomaly prediction in semiconductor fabrication presents several critical challenges.<n>The complex interdependencies between variables complicate both anomaly prediction and root-cause-analysis.<n>This paper proposes two novel approaches to advance the field from anomaly detection to anomaly prediction.
  arXiv  Detail & Related papers  (2025-10-23T16:33:52Z)
• Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition [0.0]
  dataset spans from 2003 to 2023 and includes 151,071 flare events.<n>sliding window technique is employed to detect temporal quasi-patterns in both irregular and regularized flare time series.<n>LSTM and DLSTM models are trained on sequences of peak flux and waiting times from irregular time series, while LSTM and DLSTM, integrated with an ensemble approach, are applied to sliding windows of regularized time series with a 3-hour interval.<n>DLSTM with an ensemble approach on regularized time series outperforms other models, offering more accurate large-flare forecasts with fewer false errors compared to models trained on irregular time series
  arXiv  Detail & Related papers  (2025-07-07T13:17:38Z)
• FRIREN: Beyond Trajectories -- A Spectral Lens on Time [1.5939955861266883]
  Long-term time-series forecasting models are often presented as general-purpose solutions that can be applied across domains.<n>We argue that geometric structure is the right abstraction for a dynamic-agnostic foundational model.<n>By connecting modern generative flows with classical spectral analysis, FRIREN makes long-term forecasting both accurate and interpretable.
  arXiv  Detail & Related papers  (2025-05-23T00:52:13Z)
• Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model [55.25659103706409]
  This framework achieves state-of-the-art performance for our designed foundation model, YingLong.<n>YingLong is a non-causal, bidirectional attention encoder-only transformer trained through masked token recovery.<n>We release four foundation models ranging from 6M to 300M parameters, demonstrating superior results in zero-shot tasks.
  arXiv  Detail & Related papers  (2025-05-20T14:31:06Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads [5.259027520298188]
  An Attention based Long Sort-Term Memory model (A-LSTM) is proposed to simultaneously predict traffic volume and speed. This study compares the results of the A-LSTM model with the Long Short-Term Memory (LSTM) model.
  arXiv  Detail & Related papers  (2023-01-06T22:23:57Z)
• Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction [92.16318571149553]
  We propose a multiscale-temporal graph neural network (MST-GNN) to predict the future 3D-based skeleton human poses. The MST-GNN outperforms state-of-the-art methods in both short and long-term motion prediction.
  arXiv  Detail & Related papers  (2021-08-25T14:05:37Z)
• Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models [4.56877715768796]
  Coronavirus disease 2019 (COVID-19) is a global public health crisis that has been declared a pandemic by World Health Organization. This study explores the performance of several Long Short-Term Memory (LSTM) models and Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting the number of confirmed COVID-19 cases.
  arXiv  Detail & Related papers  (2020-06-05T20:07:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.