Time series forecasting with multilayer perceptrons

I. A. Botygin; V. A. Tartakovsky; V. S. Sherstnev; A. I. Sherstneva

doi:10.1117/12.2690068

17 October 2023 Time series forecasting with multilayer perceptrons

I. A. Botygin, V. A. Tartakovsky, V. S. Sherstnev, A. I. Sherstneva

Proceedings Volume 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 1278072 (2023) https://doi.org/10.1117/12.2690068
Event: XXIX International Symposium "Atmospheric and Ocean Optics, Atmospheric Physics", 2023, Moscow, Russian Federation

Abstract

An implementation of a week-ahead air temperature and atmospheric pressure forecast using a multilayer perceptron is presented (MLP). According to the specified meteorological parameters, data preparation, implementation and performance evaluation were performed for two MLP models. The MLP architecture was a s upervised feed -forward neural network with five hidden nodes and twenty iterations (repetitions). The obtained values of the ris k function (in this case, the standard deviation of the MSE) in both implementations are quite large.

Citation Download Citation

I. A. Botygin, V. A. Tartakovsky, V. S. Sherstnev, and A. I. Sherstneva "Time series forecasting with multilayer perceptrons", Proc. SPIE 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 1278072 (17 October 2023); https://doi.org/10.1117/12.2690068

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KEYWORDS

Artificial neural networks

Data modeling

Air temperature

Atmospheric modeling

Education and training

Climatology

Environmental monitoring

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