Article ID: | iaor20043669 |
Country: | Netherlands |
Volume: | 19 |
Issue: | 4 |
Start Page Number: | 579 |
End Page Number: | 594 |
Publication Date: | Oct 2003 |
Journal: | International Journal of Forecasting |
Authors: | Gorr Wilpen, Olligschlaeger Andreas, Thompson Yvonne |
Keywords: | time series & forecasting methods |
The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be in the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.