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arXiv Machine Learning By Mary-Joe Medlej, Rahul Srinivasan, Simon Prunet, Aziz Ziad, Christophe Giordano

Probabilistic Machine Learning for Short-Term Atmospheric Seeing Forecasting

Read the original on arXiv Machine Learning →

The study evaluates machine learning models for forecasting atmospheric seeing up to two hours ahead, emphasizing probabilistic forecasting and uncertainty quantification. Gaussian Processes and a normalizing‑flow‑based model (FloTS) are compared against deterministic RNN and LSTM baselines using Maunakea summit data. Results show that deep learning models, particularly LSTM and FloTS, achieve the best predictive accuracy, with FloTS offering flexible and informative uncertainty estimates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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