Prophet-Based Research on the Medium and Long-Term Forecast Method of the F10.7 Flux of the Sun

Abstract

The F10.7 flux of the sun is an important parameter that characterizes the level of solar activity. However, due to the long-term periodicity and short-term randomness of solar activity, it is difficult to obtain accurate prediction results for F10.7 using statistical methods. The Prophet algorithm is based on time series decomposition and machine learning fitting. It can deal with the situation where there are some outliers in the time series, and it can also deal with the problem of partial missing values. F10.7 is a typical time series data, composed of two parts: time and observations, and has a history of nearly one hundred years of observation. It is inevitable that there will be some outliers and missing values in the observation process. Prophet’s data processing characteristics make it suitable for the requirements of the solar F10.7 observation data. Through reasonable selection of change points, it can realize the forecast of the future and the forecast of the seasonal trend, and finally realize the model fitting. The experimental results show that using the Prophet model to predict the consistency of F10.7 data and real data can reach more than 90%.