16. A system comprising: at least one processor; and instructions recorded on a non-transitory computer-readable medium, and executable by the at least one processor, the system including a model vector generator configured to generate a model vector for executing automated machine learning with respect to historical data, the model vector generator configured to generate the model vector through an iterative selection of values for a feature vector, a weighted model vector, and a parameter vector that comprise the model vector, the model vector generator including a weight selector configured to obtain the weighted model vector including adjusting each weight associated with a corresponding automated machine learning model of a plurality of automated machine learning models obtained from a model pool, wherein a magnitude of each weight is adjusted up or down as needed during each iteration to reflect a relative contribution of each weighted learning model to an accurate operation of the weighted model vector; a feature selector configured to obtain the feature vector including executing a meta-heuristic search of a feature solution space of a feature set of features of the historic data, the feature vector including a plurality of feature subsets, each corresponding to a weighted learning model of the weighted model vector; and a parameter selector configured to obtain the parameter vector including searching a parameter solution space of potential parameters for each learning model of the weighted model vector, wherein each iteration of the iterative selection of values for the feature vector, the weighted model vector, and the parameter vector includes the adjusting of each weight, the searching of the feature solution space, and the searching of the parameter solution space; and further wherein the system is configured to receive a forecast request related to the feature set, and provide the forecast using the model vector.
16. A system comprising: at least one processor; and instructions recorded on a non-transitory computer-readable medium, and executable by the at least one processor, the system including a model vector generator configured to generate a model vector for executing automated machine learning with respect to historical data, the model vector generator configured to generate the model vector through an iterative selection of values for a feature vector, a weighted model vector, and a parameter vector that comprise the model vector, the model vector generator including a weight selector configured to obtain the weighted model vector including adjusting each weight associated with a corresponding automated machine learning model of a plurality of automated machine learning models obtained from a model pool, wherein a magnitude of each weight is adjusted up or down as needed during each iteration to reflect a relative contribution of each weighted learning model to an accurate operation of the weighted model vector; a feature selector configured to obtain the feature vector including executing a meta-heuristic search of a feature solution space of a feature set of features of the historic data, the feature vector including a plurality of feature subsets, each corresponding to a weighted learning model of the weighted model vector; and a parameter selector configured to obtain the parameter vector including searching a parameter solution space of potential parameters for each learning model of the weighted model vector, wherein each iteration of the iterative selection of values for the feature vector, the weighted model vector, and the parameter vector includes the adjusting of each weight, the searching of the feature solution space, and the searching of the parameter solution space; and further wherein the system is configured to receive a forecast request related to the feature set, and provide the forecast using the model vector. 17. The system of claim 16 , wherein the weight selector includes a model evaluator configured to: execute the adjusting of the weights next-iteration weights, including evaluating the weights by applying each of the weighted learning models using the historical data to obtain inputs to calculate corresponding outputs; and compare the outputs against the historical data to determine an extent to which the weighted learning models were successful.