1. A computer-implemented method for generating a weighted average forecast model, comprising: receiving, using one or more processors, a plurality of forecasting models; receiving, using the one or more processors, time series data; optimizing, using the one or more processors, one or more parameters for each of the received forecasting models, wherein optimizing a parameter includes using the received time series data; determining, using the one or more processors, an information criteria value for each of the optimized forecasting models, wherein the information criteria value indicates fit quality and complexity; determining, using the one or more processors, a lowest calculated information criteria value; determining, using the one or more processors, an information criteria delta value for each of the optimized forecasting models, wherein a delta value indicates a difference between an information criteria value for an optimized forecasting model and the lowest calculated information criteria value; determining, using the one or more processors, raw weights for each of the optimized forecasting models using the information criteria delta values; determining, using the one or more processors, normalized weighting factors for each of the optimized forecasting models using the raw weights; generating, using the one or more processors, a weighted average forecast model using the optimized forecasting models and the normalized weighting factors; and generating a forecast using the weighted average forecast model.