  In many applications, a control procedure is required to detect potential
deviations in a panel of serially correlated processes. It is common that the
processes are corrupted by noise and that no prior information about the
in-control data are available for that purpose. This paper suggests a general
nonparametric monitoring scheme for supervising such a panel with time-varying
mean and variance. The method is based on a control chart designed by block
bootstrap, which does not require parametric assumptions on the distribution of
the data. The procedure is tailored to cope with strong noise, potentially
missing values and absence of in-control series, which is tackled by an
intelligent exploitation of the information in the panel. Our methodology is
completed by support vector machine procedures to estimate magnitude and form
of the encountered deviations (such as stepwise shifts or functional drifts).
This scheme, though generic in nature, is able to treat an important applied
data problem: the control of deviations in a subset of sunspot number
observations which are part of the International Sunspot Number, a world
reference for long-term solar activity.
