Related papers: A Bayesian Approach to Forecasting Solar Cycles Us…

Sunspot numbers exhibit large short-timescale (daily-monthly) variation in addition to longer timescale variation due to solar cycles. A formal statistical framework is presented for estimating and forecasting randomness in sunspot numbers…

Sunspot numbers form a comprehensive, long-duration proxy of solar activity and have been used numerous times to empirically investigate the properties of the solar cycle. A number of correlations have been discovered over the 24 cycles for…

Solar cycles are studied with the Version 2 monthly smoothed international sunspot number, the variations of which are found to be well represented by the modified logistic differential equation with four parameters: maximum cumulative…

The prediction of the strength of future solar cycles is of interest because of its practical significance for space weather and as a test of our theoretical understanding of the solar cycle. The Babcock-Leighton mechanism allows…

Sunspot activity is highly variable and challenging to forecast. Yet forecasts are important, since peak activity has profound effects on major geophysical phenomena including space weather (satellite drag, telecommunications outages) and…

This note deals with a multivariate stochastic approach to forecast the behaviour of a cyclic time series. Particular attention is devoted to the problem of the prediction of time behaviour of sunspot numbers for the current 23th cycle. The…

A new formula for predicting solar cycles based on the current theoretical understanding of the solar cycle from flux transport dynamo is presented. Two important processes---fluctuations in the Babcock-Leighton mechanism and variations in…

A review of solar cycle prediction methods and their performance is given, including forecasts for cycle 24 and focusing on aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is…

Sunspot number (SSN) is an important - albeit nuanced - parameter that can be used as an indirect measure of solar activity. Predictions of upcoming active intervals, including the peak and timing of solar maximum can have important…

The dynamic activity of the Sun, governed by its cycle of sunspots -- strongly magnetized regions that are observed on its surface -- modulate our solar system space environment creating space weather. Severe space weather leads to…

Despite the known general properties of the solar cycles, a reliable forecast of the 11-year sunspot number variations is still a problem. The difficulties are caused by the apparent chaotic behavior of the sunspot numbers from cycle to…

The prediction of solar activity is important for advanced technologies and space activities. The peak sunspot number (SSN), which can represent the solar activity, has declined continuously in the past four solar cycles (21$-$24), and the…

With recent advances in the field of machine learning, the use of deep neural networks for time series forecasting has become more prevalent. The quasi-periodic nature of the solar cycle makes it a good candidate for applying time series…

Solar variability occurs over a broad range of spatial and temporal scales, from the Sun's brightening over its lifetime to the fluctuations commonly associated with magnetic activity over minutes to years. The latter activity includes most…

Prediction of solar cycle is an important goal of Solar Physics both because it serves as a touchstone for our understanding of the sun and also because of its societal value for a space faring civilization. The task is difficult and…

Reliable prediction of the solar cycle is a formidable challenge, yet it is increasingly vital in our technology-dependent society as solar activity drives space weather. Various methods, including precursors, nonlinear curve fitting and…

Here we study the prediction of even and odd numbered sunspot cycles separately, thereby taking into account the Hale cyclicity of solar magnetism. We first show that the temporal evolution and shape of all sunspot cycles are extremely well…

Using neural networks as a prediction method, we attempt to demonstrate that forecasting of the Sun's sunspot time series can be extended to the spatial-temporal case. We employ this machine learning methodology to forecast not only in time…

The problem of prediction of a given time series is examined on the basis of recent nonlinear dynamics theories. Particular attention is devoted to forecast the amplitude and phase of one of the most common solar indicator activity, the…

The use of the spotless days to predict the future solar activity is here revised based on the new version of the sunspot number index with a 24-month filter. Data from Solar Cycle (SC) 10 are considered because from this solar cycle the…