Abstract: A system and method for generation of automated forecasts for a subject based on one or more input parameters. The subject located at an end node of a hierarchy. The method includes: receiving historical data associated with the subject; determining the sufficiency of the historical data based on a feasibility of building a machine learning model to generate a forecast with a predetermined level of accuracy using the historical data; building the machine learning model using the historical data when there is sufficiency of the historical data; building the machine learning model using historical data associated with an ancestor node on the hierarchy when there is not sufficiency of the historical data; generating a forecast for the subject using the machine learning model based on the one or more input parameters; and outputting the forecast.

Abstract: Systems and methods for constraint-based optimization, comprising: an AI demand forecasting engine, an optimization engine, a user-defined objective, and a user-defined set of constraints. Using historical sales data, the AI demand forecasting engine generates a plurality of entities, each entity defined by a placement of an item in a promotion platform; and forecasts the objective associated with each entity. The optimization engine generates a plurality of plans, each plan consisting of a unique subset of entities. Plans that violate at least one constraint are eliminated by the optimization engine, leaving a set of candidate solutions. An optimum plan is selected from the set of candidate solutions based on maximization of the objective.

Abstract: There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

Abstract: There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

Abstract: A system and method for optimizing an objective having discrete constraints using a dataset, the dataset including a plurality of aspects associated with the objective. The method comprising: receiving the dataset, the objective, and constraints, at least one of the constraints comprising discrete values; receiving a seed solution comprising initial values for the at least the constraints; iteratively performing until a predetermined threshold is reached: determining a constraint space for each of the constraints have discrete values using a determination of a constraint satisfaction problem; determining an optimized value of the objective using an optimization model, the optimization model taking as input the dataset and the constraint space; and outputting the optimized objective.

Abstract: Provided is a system and method for individual forecasting of a future event for a subject using historical data. The historical data including a plurality of historical events associated with the subject. The method including: receiving the historical data associated with the subject; determining a random variable representing a remaining time until the future event; predicting a time to the future event using a distribution function that is determined using a recurrent neural network, the distribution function including a learned density with peaks that approximate the times of the historical events in the historical data; determining a log-likelihood function based on a probability that the random variable exceeds an amount of time remaining until a next historical event in the historical data and parameterized by the distribution function; and outputting a forecast of a time to the future event as the log-likelihood function.

Abstract: Provided is a system and method for generating at least one aspect associated with a future event for a subject using historical data. The method including: determining a subject embedding using a recurrent neural network (RNN), input to the RNN includes historical events of the subject from the historical data, each historical event including by an aspect embedding, the RNN trained using aspects associated with events of similar subjects from the historical data; generating at least one aspect of the future event for the subject using a generative adversarial network (GAN), input to the GAN includes the subject embedding, the GAN trained with subject embeddings determined using the RNN for other subjects in the historical data; and outputting the at least one generated aspect.

Abstract: There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

Abstract: A system and method for model auto-selection for a prediction using an ensemble of machine learning models. The method includes: receiving historical data, the historical data including previous outcomes of a plurality of events associated with a plurality of data categories; training candidate machine learning models with the historical data, each candidate machine learning model trained using a respective one of the data categories; and determining an ensemble of machine learning models by determining a median prediction for combinations of candidate machine learning models and determining the combination that has the median prediction that is closest to at least one of the previous outcomes.

Abstract: A method and system for determining optimized customer touchpoints are provided. The method includes assigning attributes to a set of marketing content for a set of customers; generating at least one initial score for the behavior of each of the customers in the set of customers; selecting a first subset of the marketing content for each of a first subset of the customers to receive for a first marketing campaign; receiving feedback data relating to the first subset of the marketing content; determining at least one adjusted score, from the at least one initial score, for at least some of the customers; and selecting a second subset of the marketing content for each of a second subset of the customers to receive based at least partially on the at least one adjusted score.

Abstract: A system and method for generation of automated forecasts for a subject based on one or more input parameters. The subject located at an end node of a hierarchy. The method includes: receiving historical data associated with the subject; determining the sufficiency of the historical data based on a feasibility of building a machine learning model to generate a forecast with a predetermined level of accuracy using the historical data; building the machine learning model using the historical data when there is sufficiency of the historical data; building the machine learning model using historical data associated with an ancestor node on the hierarchy when there is not sufficiency of the historical data; generating a forecast for the subject using the machine learning model based on the one or more input parameters; and outputting the forecast.

Abstract: Systems and methods are presented for the computational analysis of the potential relevance of digital data items to key performance indicators. A server system imports bulk amounts of digital data from one or more disparate network-accessible digital data sources. The server system comprises an insight module configured to implement a tree-structure analysis method to identify those events in the digital data most likely to impact selected performance indicators for a given business. The results of the tree-structure analysis method are presented to the business via a user interface displayed on a computing device operated by the business. The most relevant events are presented in a distinctive manner. A recommendation module may be provided to generate recommendations from the insights.

Abstract: A method of evaluating the quality of an image of a document comprises examining the image to determine if the image satisfies at least one parameter-based image metric and examining the image to determine if the image satisfies a plurality of order-dependent image metrics.

Abstract: A method of evaluating the quality of an image of a document comprises examining the image to determine if the image satisfies at least one parameter-based image metric and examining the image to determine if the image satisfies a plurality of order-dependent image metrics.