Abstract: A method of generating a marketing offer includes: receiving first data with offers for sale of a product; adding a respective incentive to a corresponding offer, wherein: a magnitude of the respective incentive is incremented toward a corresponding maximum based on a quantity of users that have redeemed the corresponding offer; and the maximum for the respective incentive is determined based on historical data of engagement rates of users for offers with a variety of incentives.

Abstract: A computer-implemented method of generating a marketing offer for a user associated with a mobile device may include: receiving first data that includes offers for sale of a product; generating and transmitting a stream of the offers to the mobile device; adding a respective incentive to a corresponding offer in the stream, wherein: a magnitude of the respective incentive is incremented toward a corresponding maximum based on a quantity of users that have redeemed the corresponding offer; and the maximum for the respective incentive is determined based on a first machine learning model configured to optimize a net present value of offers, the first machine learning model being trained using historical data of engagement rates of users for offers with a variety of incentives; receiving historical user information associated with the user; and using a second machine learning model, selecting a particular offer from the stream and transmitting a notification to the mobile device, separate from the stream, that i

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

Abstract: Systems and methods of the present disclosure include at least one processor that receives a data set of a data stream from a data source, where the data set includes a time-varying data points. The processor determines event observations associated with data points of the time-varying data points based on a detection model to identify types of the event observations, including: i) anomalies, ii) change-points, iii) patterns, or iv) outliers. The processor generates anomaly records in an event data store based on the event observations and automatically generates event records for at least one of the anomaly records based on variables of at least one dimension of the time-varying data points, where the event record links one or more event observations. The processor automatically applies changes in the event record to each event observation of the one or more event observations based on the linking by the event record.

Abstract: Systems and methods of the present disclosure include at least one processor that receives a data set of a data stream from a data source, where the data set includes a time-varying data points. The processor determines event observations associated with data points of the time-varying data points based on a detection model to identify types of the event observations, including: i) anomalies, ii) change-points, iii) patterns, or iv) outliers. The processor generates anomaly records in an event data store based on the event observations and automatically generates event records for at least one of the anomaly records based on variables of at least one dimension of the time-varying data points, where the event record links one or more event observations. The processor automatically applies changes in the event record to each event observation of the one or more event observations based on the linking by the event record.

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

Abstract: A computer-implemented method of generating a marketing offer for a user associated with a mobile device may include: receiving first data that includes offers for sale of a product; generating and transmitting a stream of the offers to the mobile device; adding a respective incentive to a corresponding offer in the stream, wherein: a magnitude of the respective incentive is incremented toward a corresponding maximum based on a quantity of users that have redeemed the corresponding offer; and the maximum for the respective incentive is determined based on a first machine learning model configured to optimize a net present value of offers, the first machine learning model being trained using historical data of engagement rates of users for offers with a variety of incentives; receiving historical user information associated with the user; and using a second machine learning model, selecting a particular offer from the stream and transmitting a notification to the mobile device, separate from the stream, that i

Abstract: Systems and methods of the present disclosure include at least one processor that receives a data set of a data stream from a data source, where the data set includes a time-varying data points. The processor determines event observations associated with data points of the time-varying data points based on a detection model to identify types of the event observations, including: i) anomalies, ii) change-points, iii) patterns, or iv) outliers. The processor generates anomaly records in an event data store based on the event observations and automatically generates event records for at least one of the anomaly records based on variables of at least one dimension of the time-varying data points, where the event record links one or more event observations. The processor automatically applies changes in the event record to each event observation of the one or more event observations based fon the linking by the event record.