Abstract: Aspects of the subject disclosure may include, for example, segregating unique combinations for a portfolio of items into partitions; routing the partitions to an optimization service platform comprising GPUs to apply, via parallel processing by the GPUs, an optimization allocation algorithm according to parameters and analyze each of the combinations of the partitions resulting in optimal outcomes for each of the partitions; and consolidating the optimal outcomes for each of the partitions to determine a suggested portfolio selected from the items. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, segregating unique combinations for a portfolio of items into partitions; routing the partitions to an optimization service platform comprising GPUs to apply, via parallel processing by the GPUs, an optimization allocation algorithm according to parameters and analyze each of the combinations of the partitions resulting in optimal outcomes for each of the partitions; and consolidating the optimal outcomes for each of the partitions to determine a suggested portfolio selected from the items. Other embodiments are disclosed.

Abstract: System and method for generating time series forecasts based on probabilistic data are disclosed. A processor receives a request to identify at least one feature of a dataset that is most closely correlated to a single feature specified in the received request. The dataset includes the probabilistic data of the time series. The processor identifies a set of original features within the dataset and derives a degree of dependency between a single specified feature and each original feature by using a temporally first portion of the dataset. After deriving a degree of dependency between all the features including the single specified feature and each engineered feature of the set of original features, the processor identifies an original feature or an engineered feature with the highest degree of dependency as the at least one feature of the dataset that is most closely dependent to the single specified feature.

Abstract: A method may include: generating, by a computer program, random values for a plurality of pricing model parameters for a pricing model; receiving, by the computer program, a selection of a plurality of anchors; calculating, by the computer program, simulated prices for the plurality of anchors using the pricing model; training, by the computer program, encoder parameters for an encoder with the simulated prices to predict updated values for the pricing model parameters; predicting, by the pricing model using the updated values for the pricing model parameters, predicted prices for the anchors; computing, by the computer program using an error module, a loss between the simulated prices and the predicted prices; updating, by the computer program, the encoder parameters based on the loss; and deploying, by the computer program, the pricing model to a production environment in response to the loss being within a threshold range.