Kinaxis Inc.

Sysomos Inc.

Kanchana Padmanabhan

Brian Keng

Edward Dong-Jin Kim

Brian Jia-Lee Keng

Fan Zhang

Anneya Golob

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.

Method and system for model auto-selection using an ensemble of machine learning models

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.

Constraint-based optimization

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.

Method and system for generation of at least one output analytic for a promotion

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.

Method and system for hierarchical forecasting

System and methods performed by a server for determining weighted influence in social networks, including: determining posts related to the topic; characterizing each post as one or more of: a reply post, a mention post, and a re-posting; generating a group of user accounts comprising any user account that authored a posting to which is replied, being mentioned in the mention post, that posted content being re-posted, and/or that authored one or more posts that are related to the topic; representing each of the user accounts as a node in the group in a connected graph and establishing an edge between one or more pair of nodes when there is a follower-followee relationship between the nodes; and for each edge between nodes, determining a weighting that is a function of one or more of: a number of mention posts, a number of reply posts, and a number of re-posts.

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Toronto, Canada

Kanchana Padmanabhan