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Wireless systems are subject to fading - time variations of the receiving conditions caused by multipath propagation and transceiver movements. Prediction of fading allows to 'learn' the channel state information (CSI) in advance and adjust the transmission scheme accordingly. In this contribution we consider a framework to handle predictions of general fast- and non-flat fading MIMO wireless channels. The approach is based on modeling the dynamics of individual multipath components, extracted with the SAGE algorithm. This decreases the rate of variation of the channel thus allowing a greater prediction horizon and simpler predictor designs. The extracted components are then tracked using dynamical programming coupled with the multipath component distance measure, and component parameters are then predicted over time using adaptive predictors - hypermodels. We consider linear as well as nonlinear predictor designs. This prediction scheme is applied to MIMO impulse response measurements in 2 GHz frequency band, tracked over the distance of ap 4 m, achieving prediction horizons of 1.5lambda.