Predicción del ciclo solar 25 mediante modelos ARIMA y redes neuronales LSTM
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Tomas, S., Saavedra , O. ., & Espinoza, I. . (2023). Predicción del ciclo solar 25 mediante modelos ARIMA y redes neuronales LSTM. Revista De La Academia Colombiana De Ciencias Exactas, Físicas Y Naturales, 47(183), 400–411. https://doi.org/10.18257/raccefyn.1849

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Se realizó un estudio para predecir el número de manchas solares en el Ciclo Solar 25 mediante el uso de dos modelos: un modelo de redes neuronales recurrentes Long short-term memory (LSTM) y un modelo Autoregressive Integrated Moving Average (ARIMA). Los datos utilizados para entrenar los modelos fueron obtenidos del sitio web del Centro Mundial de Datos SILSO, del Real Observatorio de Bélgica en Bruselas, desde 1749 hasta 2018. Nuestro modelo LSTM demostró un rendimiento excepcional (RMSE=3,6) en comparación con el mejor modelo ARIMA (RMSE=32,6). Esto demostró que nuestro modelo LSTM es significativamente más preciso en términos de predicción, con una mejora del 89% en la reducción del RMSE. Según nuestro modelo LSTM, se prevé que el número máximo de manchas solares en el Ciclo Solar 25 ocurra en marzo de 2025, alcanzando un valor máximo de 182 manchas solares. En contraste, el modelo ARIMA predice que el máximo se alcanzará en diciembre de 2024, con un valor máximo de 99 manchas solares.

https://doi.org/10.18257/raccefyn.1849

Palabras clave

Ciclo Solar 25 | Manchas solares | Redes neuronales recurrentes | Predicción
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Derechos de autor 2023 Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales