Speaker
Amal Saadallah
(Lamarr Institute-TU Dortmund)
Description
The study of sunspot numbers is crucial for understanding solar activity and its impact on Earth's climate and space weather. This research analyzes the temporal patterns in historical sunspot data and develops predictive models for long-term forecasting. Using statistical and deep learning techniques, we identify key trends, periodicities, and anomalies in sunspot cycles. The proposed models are evaluated using error metrics such as RMSE and SMAPE to assess their predictive accuracy. Our findings provide insights into solar cycle variations and contribute to improving forecasts of solar activity for scientific and practical applications.
