Time series forecasting constitutes an important aspect of any kind of technical system, since the underlying stochastic processes vary over time. Extensive efforts for designing self-adaptive learning systems have been made, to take system designers out of the loop. One goal of such systems is to transfer design-time decisions, e.g. parametrisation, to the run-time. By means of forecasting the succeeding system state, the system itself is enabled to anticipate, how to reconfigure to handle upcoming conditions. Ensemble forecasting is a specific means of combining and weighting the forecasts of multiple independent forecast methods. This concept has proven successful in various domains today. In this work, we present our self-adaptive forecast module for ensemble forecasting of univariate time series and draw a picture of how the eXtended Classifier System for Function approximation (XCSF) can be utilised as a novel weighting approach in this context. We elaborate on the fundamental ideas and evaluate our proposed technique on the basis of several time series with different characteristics.