Grant: Engage Plus Grants Program
University of Regina
$12,500.00
Predictive and visual analytics for strategic business planning
FY 2017-18
Summary
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. The main objectives of the research project are (1) to improve 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 improve a dashboard that displays information about credit union health and performance in an easy to understand format. Given an initial model based on auto-regressive integrated moving averages (ARIMA), a wide variety of predictive analytics techniques including vector auto-regression (VAR), machine learning (classification learning and deep learning), and data mining will be evaluated to determine whether they improve predictive accuracy. The dashboard will be improved to display the input and summarized output for a large variety of data sources. Research will be performed to determine an appropriate way for the graphical user interface (GUI) to display alerts for cases where the actual economic and business performance significantly deviates from forecasts. This information will be given for individual credit unions and also presented in the context of aggregated values. In the evaluation part of the research, first the improved predictive model will be implemented and verified using some artificial data. Next, we will apply the improved model to historical data to determine its effectiveness. A variety of conditions will be simulated to ensure that alerts are produced. 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. Finally, the software prototype will be evaluated in association with Celero personnel in the context of performing strategic business planning for the next year.
Program
Engage Plus Grants Program
Recipient
Location
Saskatchewan, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
518096-2017
Name nom
Hamilton, Howard
Department département
Computer Science
Organization id
17
Institution établissement
University of Regina
Province en
Saskatchewan
Province fr
Saskatchewan
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2017
Award amount
12500
Program id
EGP2
Program name en
Engage Plus Grants Program
Program name fr
Programme de subvention d'engagement partenarial Plus
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1555
Committee name en
Prairie Internal Decision Committee
Committee name fr
Comité de décision interne pour les Prairies
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
2805
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Learning and inference theories
Research subject fr
Théories de l'apprentissage et l'inférence
Partie
1
Nb partie
1
Application title
Predictive and visual analytics for strategic business planning
Keyword
NSERC
Application summary
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. The main objectives of the research project are (1) to improve 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 improve a dashboard that displays information about credit union health and performance in an easy to understand format. Given an initial model based on auto-regressive integrated moving averages (ARIMA), a wide variety of predictive analytics techniques including vector auto-regression (VAR), machine learning (classification learning and deep learning), and data mining will be evaluated to determine whether they improve predictive accuracy. The dashboard will be improved to display the input and summarized output for a large variety of data sources. Research will be performed to determine an appropriate way for the graphical user interface (GUI) to display alerts for cases where the actual economic and business performance significantly deviates from forecasts. This information will be given for individual credit unions and also presented in the context of aggregated values. In the evaluation part of the research, first the improved predictive model will be implemented and verified using some artificial data. Next, we will apply the improved model to historical data to determine its effectiveness. A variety of conditions will be simulated to ensure that alerts are produced. 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. Finally, the software prototype will be evaluated in association with Celero personnel in the context of performing strategic business planning for the next year.
