  Building a reliable forecast of solar activity is a long-standing problem
that requires to accurately describe past and current global dynamics. However,
synoptic observations of magnetic fields and subsurface flows became available
relatively recently. In this paper, we present an investigation of effects of
short observational data series on accuracy of solar cycle prediction. This
analysis is performed using the annual sunspot number time-series applied to
the Parker-Kleeorin-Ruzmaikin dynamo model and employing the Ensemble Kalman
Filter (EnKF) data assimilation method. The testing of the cycle prediction
accuracy is performed for the last six cycles (from Solar Cycle 19 to 24) by
sequentially shortening the observational data series that are used for
prediction of a target cycle, and evaluating the prediction accuracy according
to specified criteria. According to the analysis, reliable activity predictions
can be made using relatively short time-series of the sunspot number. It
demonstrated that even two cycles of available observations allow us to obtain
reasonable forecasts.
