Kinaxis has been granted a patent for a system and method that automates forecasting for a subject based on input parameters. The method involves analyzing historical data, building machine learning models, and generating forecasts using a hierarchical structure. The patent aims to improve accuracy and efficiency in forecasting processes. GlobalData’s report on Kinaxis gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Kinaxis, Predictive modeling techniques was a key innovation area identified from patents. Kinaxis's grant share as of February 2024 was 25%. Grant share is based on the ratio of number of grants to total number of patents.
Automated forecasting system using machine learning models
A recently granted patent (Publication Number: US11928616B2) discloses a computer-implemented method and system for generating automated forecasts for a subject based on input parameters. The method involves receiving historical data associated with the subject, determining its insufficiency, obtaining historical data from ancestor nodes or similar subjects, building a machine learning model using various models like Random Forest and Linear Regression, and generating forecasts using a meta-machine learning model. The system includes an analysis module to assess data sufficiency and a forecasting module to build and train machine learning models, combining predictions to generate forecasts.
Furthermore, the patent claims cover scenarios where historical data is insufficient for the subject and ancestor nodes, requiring data only from similar subjects. The system also considers building machine learning models based on historical data associated with similar nodes in the categorical hierarchy. Additionally, the patent emphasizes the pooling of historical data from ancestor nodes and similar subjects to effectively build and train the machine learning model for accurate forecasting. The method and system outlined in the patent aim to enhance the accuracy and reliability of automated forecasts by leveraging historical data from related nodes in the categorical hierarchy.
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