Solar observational studies are crucial for understanding the sun's behaviour, its impact on space weather, and its influence on Earth's climate. Central to this research is sunspot data analysis, a key indicator of solar activity and magnetic field variations. The study of solar differential rotation has been fundamental, with pioneering work revealing that faster equatorial rotation influences the sun's magnetic field and activity cycle. Sunspot areas, meticulously documented by observatories like the Royal Greenwich Observatory and KoSO, have been critical for analysing long-term solar activity trends. The integration of machine learning has significantly advanced sunspot data analysis, enhancing space weather forecasting and the understanding of solar phenomena. This paper employs change point analysis on KoSO sunspot and umbra area data to detect significant shifts over time, utilising nonparametric methods for their computational efficiency. Results show deviations from normality, positive trends, and significant autocorrelation in the data. The PELT algorithm reveals several significant shifts, dividing the period into distinct segments with varying statistical characteristics. These findings align with known solar cycles and highlight the importance of advanced statistical techniques in understanding solar activity.
Jana et al. (Thu,) studied this question.