Modeling Sunspot Activity Variability and Autoregulation in the Period 2000-2018 with Advanced Statistical Approach
(1) Universitas Negeri Medan, Medan, Indonesia
(*) Corresponding Author
Abstract
This study analyzes sunspot activity data from 2000 to 2018 to identify patterns and factors influencing fluctuations in solar activity, which has implications for space weather and global climate. The study focuses on parameters such as mu, omega, α1 (autoregressive), and β1 (moving average), hypothesizing that sunspot activity exhibits significant variability and can be predicted using modified ARMA models. The research employs statistical analysis of ARMA model parameters, including significance tests, serial correlation, and heteroscedasticity analysis, along with stability tests (Nyblom) and sign bias evaluation. Results show that mu and omega parameters significantly influence sunspot activity, with high t-statistics. The autoregressive coefficient α1 strongly predicts future activity, while β1 (moving average) has minimal impact. Findings confirm that sunspot activity is volatile, dependent on past values, and exhibits serial correlation and heteroscedastic volatility. The study underscores the need for more advanced models, such as ARIMA or AI-based approaches, to improve predictive accuracy. Autoregressive modeling proved effective, while moving averages showed limited contribution.
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