Прогнозування сонячної активності альтернативними методами

• Дата: 2018
• Автори: Bidyuk, Petro I., Karayuz, Iryna V., Varava, Vlad V., Jirov, Oleksandr L.
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2018
• Теми: Adaptive Kalman Filter; Optimal Kalman Filter; Maximum Likelihood Method; Probabilistic Granular Filter; Sun Activity; Short Term Forecasting; адаптивный фильтр Калмана; оптимальный фильтр Калмана; метод максимального правдоподобия; вероятностный гранулярный фильтр; солнечная активность; краткосрочное прогнозирование; адаптивний фільтр Калмана; оптимальний фільтр Калмана; метод максимальної правдоподібності; ймовірнісний гранулярний фільтр; соняшна активність; короткострокове прогнозування
• Онлайн доступ: http://journal.iasa.kpi.ua/article/view/152279
• Назва журналу: System research and information technologies

The study is focused on the problem of forecasting nonstationary processes of solar activity using alternative procedures. The problem is urgent and it is considered by groups of researchers in many countries of the world. The processes under study belong to the class of nonlinear and nonstationary which requires selecting special methods for their modeling and forecasting. The study proposes an approach to forecasting based on three filters: the adaptive Kalman filter, optimal Kalman filter with parameter estimation using the maximum likelihood procedure and probabilistic particle filter. Selection of the filters is substantiated by the fact that they provide a possibility for taking into consideration stochastic external disturbances and measurement errors. The results of computational experiments showed the support for the idea that the methods selected are suitable for solving the problem stated. The best results of short-term forecasting of exponentially smoothed data were achieved using an adaptive filter. The analysis of results was performed by employing the known statistical quality characteristics including the mean absolute percentage error.