Aggregate Function Generalization to Temporal Data

Abstract

In this article, we define an approximate generalization of aggregate functions for relational data with temporal attributes. This generalization is parametrized to allow simulation of a range of common aggregate functions and optionally take into account time. The parameters are not optimized, but we rather rely on repeated stochastic sampling of the parameters. We then apply a common regularized linear model to train a model on this high-dimensional space. Experimental results on 11 datasets suggest that there are datasets where incorporating time dimension into the model leads to an improvement in the predictive accuracy of the trained models.