12. A computer-implemented method for executing instructions stored on a non-transitory computer readable storage medium, the method comprising: determining a model vector <d t h , w t h , δ t h >, in which d t h represents a feature vector including t feature subsets of a feature set, w t h represents a weighted model vector including t weighted automated learning models, and δ t h represents t parameter sets parameterizing the w t h weighted automated learning models; adjusting weights of w t h to obtain an updated w t h , w t h+1 , based on performance evaluations of the t weighted automated learning models, and based on w t h ; searching a feature solution space to obtain t updated feature subsets of the feature set, to thereby obtain an updated d t h , d t h+1 ; searching a parameter solution space to obtain t updated parameter sets, to thereby obtain an updated δ t h , δ t h+1 ; determining an optimized model vector (d t h+1 , w t h+1 , δ t h+1 ); receiving a forecast request for a forecast related to the feature set; and providing the forecast, using the optimized model vector (d t h+1 , w t h+1 , w t h+1 ).
12. A computer-implemented method for executing instructions stored on a non-transitory computer readable storage medium, the method comprising: determining a model vector <d t h , w t h , δ t h >, in which d t h represents a feature vector including t feature subsets of a feature set, w t h represents a weighted model vector including t weighted automated learning models, and δ t h represents t parameter sets parameterizing the w t h weighted automated learning models; adjusting weights of w t h to obtain an updated w t h , w t h+1 , based on performance evaluations of the t weighted automated learning models, and based on w t h ; searching a feature solution space to obtain t updated feature subsets of the feature set, to thereby obtain an updated d t h , d t h+1 ; searching a parameter solution space to obtain t updated parameter sets, to thereby obtain an updated δ t h , δ t h+1 ; determining an optimized model vector (d t h+1 , w t h+1 , δ t h+1 ); receiving a forecast request for a forecast related to the feature set; and providing the forecast, using the optimized model vector (d t h+1 , w t h+1 , w t h+1 ). 15. The method of claim 12 , wherein the feature solution space and the parameter solution space are searched synchronously for current weight vector w t h .