Sunspots are solar features located in active regions of the Sun, whose number is an indicator of the Sun’s magnetic activity. Therefore accurate detection and classification of sunspots are fundamental for the elaboration of solar activity indices such as the Wolf number. However, irregularities in the shape of the sunspots and their variable intensity and contrast with the surroundings, make ...

Time series of high-resolution images of the complex active region NOAA 10786 are studied. The observations were performed in G-band (430.5 nm) and in the nearby continuum (463.3 nm), on July 9, 2005 at the Swedish 1-meter Solar Telecope (SST) in La Palma. Granular proper motions in the surroundings of the sunspots have been quantified. A large-scale radial outflow in the velocity range 0.3 1 k...

This paper describes automatic sunspot recognition and classification from satellite images. Some experimental results on learning sunspot classification using data mining techniques are presented. The classification scheme attempted was the seven-class Modified Zurich scheme. Daily images of the solar disk were taken from the NASA SOHO satellite’s MDI instrument and sunspots extracted using im...

We analyze a simple sunspot model that represents a standard securities market without sidebets on an exogenous sunspot event. The multiple self–fulfilling equilibria that arise in the model are based on investors’ uncertainty about other investors’ beliefs. Hence, these multiple equilibria are “endogenous sunspots.” We show that endogenous sunspots can arise even with complete markets to which...

This paper shows that imperfect competition can be a rich source of sunspots equilibria and coordination failures. This is demonstrated in a dynamic general equilibrium model that has no major distortions except imperfect competition. In the absence of fundamental shocks, the model has a unique certainty (fundamental) equilibrium. But there is also a continuum of stochastic (sunspots) equilibri...

This paper uses a stylised asset-pricing model to show that sunspots may cause asset returns to be predictable, a widely documented feature of many speculative markets. This result parallels and extends previous works showing that sunspots render asset prices excessively volatile. Journal of Economic Literature Classi…cation Numbers: D84, E44, G12

Accurate prediction of ionospheric parameters is crucial for telecom-munication companies. These parameters strongly rely on solar activity. In this paper, we analyze the use of neural networks for sunspots time series prediction. Three types of models are tested and experimental results are reported for a particular sunspots time series: the IR5 index.

The paper ...nds empirical evidence on the ripple e¤ect of sunspots on the interwar German economy. It identi...es a sequence of negative shocks to expectations for the 1927 to 1932 period. The arti...cial economy predicts the 1928-1932 depression and a long boom from 1933 onwards. Overall, a tangible fraction of interwar output volatility is attributed for by sunspots.