Ensemble Time Series Forecasting with XCSF
Matthias Sommer, Anthony Stein, Jörg Hähner · 2016
Time series forecasting constitutes an important aspect of any technical system, since the underlying data generating processes vary over time. In order to take system designers out of the loop, efforts for designing self-adaptive, learning systems have extensively been made. By means of forecasting the succeeding system state, the system is enabled to anticipate how to reconfigure itself for satisfying the upcoming conditions. Ensembleforecasting is a specific means of combining the forecasts of multiple independent forecast methods. In this work, we draw a picture of how the eXtended Classifier System for Function approximation (XCSF) can be utilised as a novel self-adaptive weighting approach for ensemble forecasting of univariate time series. We elaborate on the fundamental ideas and evaluate our proposed technique on the basis of several time series.