Abstract: Systems, software, and methods are disclosed for generating a prediction of time-series data from data sets. A system is configured to: retrieve, for each product of a set of products, the time-series data including time-value pairs; select a first product; compute a correlation value between the first product and other products and for one or more degrees of lag to obtain a set of correlation values representing the correlations between the first product to the other products assessed at prior times; select a subset of products based at least in part on the correlation values; provide the time-series data associated with each product from the subset of products and the first product to a machine learning model trained to predict a future value of the first product based on values of the subset of products; and obtain prediction data representing a set of predicted values for the first product.

Abstract: Methods, apparatuses, and computer program products are provided that utilize specially configured model-based trees to output data that more accurately satisfies a particular predictive task. Each model-based tree may include any number of branches partitioning an original data set into subsets for processing. Each branch may end in one or more leaf nodes, where each leaf node is associated with a data model specially configured based on a particular set of partitioned data associated with the leaf node. In this regard, the plurality of model-based trees may generate data utilized to determine and/or output preferred data for a particular user.

Abstract: Provided herein are method, apparatus, and computer program products for facilitating the determination of an ideal discount rate or an ideal accepted value for a promotion based on a plurality of model-based decision trees. The decision trees may be generated based on a set of original data, which may include information from a set of past promotions. The trees may branch into smaller, more homogenous subgroups of the original data until a model is generated for each resulting node of the tree. The plurality of trees may be applied to a current promotion with a set of possible predetermined discount rates in order to determine an ideal discount rate or an ideal accepted value for the promotion.