Reference number : GC-2017-Q1-00368, amendment number : , agreement type : G, recipient legal name : Hamilton, Howard (University of Regina), recipient country : CA, recipient province : nan, recipient city : Saskatchewan, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Celero Solutions requires research on predictive and visual analytics for strategic business planning.  A particular concern is making predictions about the Canadian economy and especially the business of Canadian credit unions._x000D_
     The main objectives of the research project are (1) to design and evaluate a predictive model of the Canadian credit union business that incorporates publicly available and private economic data concerning global, national, and local economic factors, and (2) to create and evaluate a dashboard that displays information about credit union health and performance in an easy to understand format. _x000D_
     A wide variety of predictive analytics techniques, including statistical regression, auto-regressive integrated moving averages (ARIMA), vector auto-regression (VAR), machine learning (classification learning and deep learning), and data mining will be evaluated for their suitability to the predictive task. The dashboard will be designed to display the input and summarized output related to the economic model.  The user will be able to select an appropriate segment of the credit union business. Research will be performed to determine an appropriate way for the graphical user interface (GUI) to display appropriate information depending on the market segment under consideration. This information will be given for individual credit unions and also presented in the context of aggregated values for the whole market segment._x000D_
     In the evaluation part of the research, first the predictive model will be implemented and verified using some artificial data. Next, we will apply the model to historical data to determine its effectiveness. A methodology of train-and-test will be used, whereby only the older data is used for training (creating the model) and the newer data is used for testing. Separately, as part of the evaluation, we will perform detailed software testing on the dashboard to ensure that all required functionality is present and works correctly and reliability. _x000D_
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_x000D_, program purpose french : Celero Solutions requires research on predictive and visual analytics for strategic business planning.  A particular concern is making predictions about the Canadian economy and especially the business of Canadian credit unions._x000D_
     The main objectives of the research project are (1) to design and evaluate a predictive model of the Canadian credit union business that incorporates publicly available and private economic data concerning global, national, and local economic factors, and (2) to create and evaluate a dashboard that displays information about credit union health and performance in an easy to understand format. _x000D_
     A wide variety of predictive analytics techniques, including statistical regression, auto-regressive integrated moving averages (ARIMA), vector auto-regression (VAR), machine learning (classification learning and deep learning), and data mining will be evaluated for their suitability to the predictive task. The dashboard will be designed to display the input and summarized output related to the economic model.  The user will be able to select an appropriate segment of the credit union business. Research will be performed to determine an appropriate way for the graphical user interface (GUI) to display appropriate information depending on the market segment under consideration. This information will be given for individual credit unions and also presented in the context of aggregated values for the whole market segment._x000D_
     In the evaluation part of the research, first the predictive model will be implemented and verified using some artificial data. Next, we will apply the model to historical data to determine its effectiveness. A methodology of train-and-test will be used, whereby only the older data is used for training (creating the model) and the newer data is used for testing. Separately, as part of the evaluation, we will perform detailed software testing on the dashboard to ensure that all required functionality is present and works correctly and reliability. _x000D_
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_x000D_, agreement title english : Preliminary predictive and visual analytics for strategic business planning, agreement title french : Preliminary predictive and visual analytics for strategic business planning, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada