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Since the AAM model was developed, both the data management technology for storing and manipulating very large data sets in distributed data platforms and the availability of sophisticated machine learning algorithms designed to run in a parallel processing environment have increased rapidly. Based on our recent experience, it reduces the variable identification, transformation and selection, modeling development, and validation time. Currently, we are using this technology to design new predictors, test different data structures, and explore alternative modeling algorithms to update the AAM model.

In particular, we will be testing the use of rates of change as additional predictors into the model.