Repository:https://github.com/UBC-MDS/project-avalonReport link:https://github.com/UBC-MDS/project-avalon/blob/main/docs/crime_forecasting.html
Abstract/executive summary:
Summary
In this project, our focus revolved around constructing a time-series forecasting model tailored to predict crime incidents in Vancouver, using "Month" as the temporal unit. Our primary emphasis centered on one of the most prevalent crime types in Vancouver over the past two decades: theft from vehicles. We evaluated the efficacy of three fundamental forecasting models—simple moving average, exponential smoothing, and ARIMA(1,1,0). Moreover, the ARIMA(1,1,0) model's performance suggests promising potential for practical deployment in crime prevention strategies by law enforcement agencies.
Submitting authors: @SoloSynth1 @phchen5 @MoNorouzi23 @zywkloo
Repository:https://github.com/UBC-MDS/project-avalon Report link:https://github.com/UBC-MDS/project-avalon/blob/main/docs/crime_forecasting.html Abstract/executive summary: Summary In this project, our focus revolved around constructing a time-series forecasting model tailored to predict crime incidents in Vancouver, using "Month" as the temporal unit. Our primary emphasis centered on one of the most prevalent crime types in Vancouver over the past two decades: theft from vehicles. We evaluated the efficacy of three fundamental forecasting models—simple moving average, exponential smoothing, and ARIMA(1,1,0). Moreover, the ARIMA(1,1,0) model's performance suggests promising potential for practical deployment in crime prevention strategies by law enforcement agencies.
Editor: @MoNorouzi23 Reviewer: Sifan Zhang, Yin Huang, Atabak Alishiri,