Multidimensional hierarchy level recommendation for forecasting models

Abstract

Provided is a system and method for identifying and recommending the best hierarchical levels for training a predictive model such as a time-series forecasting model. In one example, the method may include receiving an identification of a measure of multidimensional data, generating a plurality of training data sets that comprise different combinations of hierarchical dimension granularities of aggregation, training a plurality of instances of a machine learning model based on the plurality of training data sets, respectively, and determining and outputting predictive accuracy values of the plurality of instances of the trained machine learning model.