Local ensemble weighting in the context of time series forecasting using XCSF
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.
2016 IEEE Symposium Series on Computational Intelligence (SSCI)