A software toolbox for constructing ensembles of heterogenous linear and nonlinear models

We introduce a software toolbox for building ensembles of computer models based on measured time series. The toolbox is using statistical learning techniques for training of individual linear and nonlinear models as well as the construction of ensembles of heterogenous models types. Several well performing model types among which are ridge regression, k-nearest neighbor models and neural networks have been implemented. Ensembles of heterogenous models typically give better performance then any other class. Additionally, methods for validation and model assessment are given as well as adaptor classes perform a transparent feature selection or the training random subsets of all input variables. The toolbox is implemented in Matlab and C++.