All Times ET
Keywords: autocorrelation, neural network, sunspot, quality control
The observation of sunspots is one of the most important empirical data source giving information about the long-term solar activity. The sunspots observation extends from the seventeenth century to the present day. Surprisingly, determining the number of sunspots consistently over time remains a challenging problem. The challenge involves the absence of stationarity, different types of correlation and many kinds of observational errors. In this work, we construct an artificial neural network for monitoring these important series. The method is trained by simulations that are sufficiently general to allow the predictions on unseen deviations of various types. The procedure can efficiently detect when the observations are deviating and takes into account the autocorrelation of the data. The network has been compared to a more classical procedure based on the CUSUM chart and appears to be consistent with the latter. It can also predict the size of the encountered deviations over a large range of values. Using this method allows us to detect and identify a wide range of deviations. Many of these deviations are observer or equipment related. Detecting and understanding them will help improve future observations. Eliminating or correcting them in past data will lead to a more precise reconstruction of the International Sunspot Number, the world reference for solar activity.