1. A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed, are configured to cause at least one processor to: determine 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; adjust 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 ; search 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 , search a parameter solution space to obtain t updated parameter sets, to thereby obtain an updated δ t h , δ t h+1 ; determine an optimized model vector (d t h+1 , w t h+1 , w t h+1 ); receive a forecast request for a forecast related to the feature set; and provide the forecast, using the optimized model vector (d t h+1 , w t h+1 , w t h+1 ).
11. The computer program product of claim 1 , wherein the instructions, when executed, are configured to cause the at least one processor to: search the feature solution space and the parameter solution space synchronously for current weight vector w t h .