Abstract: Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user.

Abstract: Systems and methods for forecasting future values of data streams are disclosed. One example method may include receiving information characterizing each of a plurality of forecasting models, retrieving historical data for each of a plurality of data streams, determining one or more constraints, dynamically selecting one of the plurality of forecasting models for each of the data streams based on accuracy metrics for the forecasting models, estimating cost metrics associated with each forecasting model, dynamically selecting the forecasting model based at least in part on the accuracy metrics, the cost metrics, and the determined constraints, and forecasting a first subsequent value of each data stream using the corresponding selected forecasting model.

Abstract: This disclosure relates to forecasting when and whether an invoice is to be paid and indicating such forecasts to a user. An example system is configured to perform operations including determining, by a classification model, a first confidence as to whether an invoice is to be paid, determining, by a regression model associated with the classification model, a first time associated with a second confidence as to when the invoice is likely to be paid, and indicating, to a user, whether the invoice is to be paid based on the first confidence and the first time when the invoice is likely to be paid based on the second confidence. The regression model may include one or more gradient boosted trees to determine the first time. Different times associated with different confidences can be determined by different gradient boosted trees, with the specific tree corresponding to the associated confidence.

Abstract: Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user.

Abstract: Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user.

Abstract: This disclosure relates to forecasting when and whether an invoice is to be paid and indicating such forecasts to a user. An example system is configured to perform operations including determining, by a classification model, a first confidence as to whether an invoice is to be paid, determining, by a regression model associated with the classification model, a first time associated with a second confidence as to when the invoice is likely to be paid, and indicating, to a user, whether the invoice is to be paid based on the first confidence and the first time when the invoice is likely to be paid based on the second confidence. The regression model may include one or more gradient boosted trees to determine the first time. Different times associated with different confidences can be determined by different gradient boosted trees, with the specific tree corresponding to the associated confidence.

Abstract: Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user.

Abstract: Systems and methods for forecasting future values of data streams are disclosed. One example method may include receiving information characterizing each of a plurality of forecasting models, retrieving historical data for each of a plurality of data streams, determining one or more constraints, dynamically selecting one of the plurality of forecasting models for each of the data streams based on accuracy metrics for the forecasting models, estimating cost metrics associated with each forecasting model, dynamically selecting the forecasting model based at least in part on the accuracy metrics, the cost metrics, and the determined constraints, and forecasting a first subsequent value of each data stream using the corresponding selected forecasting model.

Abstract: Certain aspects of the present disclosure provide techniques for providing a diagnostics framework for large scale hierarchical time series forecasting models.

Abstract: The disclosed embodiments provide a system that facilitates testing of an insecure computing environment. During operation, the system obtains a real data set comprising a set of data strings. Next, the system determines a set of frequency distributions associated with the set of data strings. The system then generates a test data set from the real data set, wherein the test data set comprises a set of random data strings that conforms to the set of frequency distributions. Finally, the system tests the insecure computing environment using the test data set.

Abstract: The disclosed embodiments provide a system that facilitates selecting a message to be presented to users based on a statistically valid hypothesis test. During operation, the system runs a hypothesis test by presenting alternate versions of a message to a test set of users and receives user-feedback data. Next, the system obtains a significance level for the test and determines a number of independent data subsets associated with data from the test. The system subsequently uses the significance level and the number of independent data subsets to calculate an individual significance level for each independent data subset. The system then uses the individual significance levels to calculate an amount of user-feedback data required to achieve the significance level during the test, and selects one of the alternate versions of the message by analyzing the calculated amount of user-feedback data during the test. Finally, the system presents the selected version.

Abstract: The disclosed embodiments provide a system that facilitates testing of an insecure computing environment. During operation, the system obtains a real data set comprising a set of data strings. Next, the system determines a set of frequency distributions associated with the set of data strings. The system then generates a test data set from the real data set, wherein the test data set comprises a set of random data strings that conforms to the set of frequency distributions. Finally, the system tests the insecure computing environment using the test data set.

Abstract: The disclosed embodiments provide a system that facilitates testing of an insecure computing environment. During operation, the system obtains a real data set comprising a set of data strings. Next, the system determines a set of frequency distributions associated with the set of data strings. The system then generates a test data set from the real data set, wherein the test data set comprises a set of random data strings that conforms to the set of frequency distributions. Finally, the system tests the insecure computing environment using the test data set.

Abstract: The disclosed embodiments provide a system that facilitates testing of an insecure computing environment. During operation, the system obtains a real data set comprising a set of data strings. Next, the system determines a set of frequency distributions associated with the set of data strings. The system then generates a test data set from the real data set, wherein the test data set comprises a set of random data strings that conforms to the set of frequency distributions. Finally, the system tests the insecure computing environment using the test data set.