ROC-Based Model Estimation for Forecasting Large Changes in Demand

Forecasting for large changes in demand should benefit from different estimation than that used for estimating mean behavior. We develop a multivariate forecasting model designed for detecting the largest changes across many time series. The model is fit based upon a penalty function that maximizes true positive rates along a relevant false positive rate range and can be used by managers wishing to take action on a small percentage of products likely to change the most in the next time period. We apply the model to a crime dataset and compare results to OLS as the basis for comparisons as well as models that are promising for exceptional demand forecasting such as quantile regression, synthetic data from a Bayesian model, and a power loss model. Using the Partial Area Under the Curve (PAUC) metric, our results show statistical significance, a 35 percent improvement over OLS, and at least a 20 percent improvement over competing methods. We suggest management with an increasing number of products to use our method for forecasting large changes in conjunction with typical magnitude-based methods for forecasting expected demand

Forecast accuracy measures for exception reporting using receiver operating characteristic curves

The exception principle of management reporting suggests that, under ordinary conditions, operational staff persons make decisions, but that the same staff refer decisions to upper-level managers under exceptional conditions. Forecasts of large changes or extreme values in product or service demand are potential triggers for such reporting. Seasonality estimates in univariate forecast models and leading independent variables in multivariate forecast models are among the approaches to forecasting exceptional demand, a forecast activity that this paper identifies as requiring new accuracy measures based on the tails of sampled forecast error distributions, rather than conventional measures which use the central tendency. For this purpose, the paper introduces the application of the receiver operating characteristic (ROC) framework, which has been used for the assessment of exceptional behavior in many fields. In a case study on serious violent crime in Pittsburgh, Pennsylvania, the simplest, non-naïve univariate forecast method is best for forecasting ordinary conditions using conventional forecast accuracy measures, but the most complex multivariate model is best for forecasting exceptional conditions using ROC forecast accuracy measures.Forecast accuracy measures Exception reporting ROC curves Crime forecasting

Early Warning System for Temporary Crime Hot Spots

Objectives: We investigate the potential for preventing crimes at temporary hot spots in addition to chronic hot spots. Using data on serious violent crimes from Pittsburgh, Pennsylvania, we investigate an early warning system (EWS) for starting/stopping police deployments at temporary hot spots in coordination with constant prevention work at chronic hot spots. Methods: We estimate chronic hot spots using kernel density smoothing. We use simple rules for detecting flare-ups of temporary hot spots, predicting their persistence, deploying police, and stopping deployments. We also consider a combination program including the hottest chronic hot spots, with EWS applied to remaining areas. Using 2000–2010 data, we run computational experiments varying the size of chronic hot spots and varying rule thresholds to tune the EWS. Tradeoff curves with percentage of crimes exposed to prevention versus percentage area of the city with crime prevention workload provide tools for coordinating chronic and temporary hot spot programs. Results: The combination program is the most efficient, equitable, and responsive program. After first allocating police prevention resources to the hottest chronic hot spots, the marginal benefits of adding more chronic hot spot area is not as high as adding temporary hot spots. Chronic hot spots are limited to large commercial and adjoining residential areas. Temporary hot spots are widely scattered throughout Pittsburgh. Conclusions: Temporary hot spots exist outside of chronic hot spots and are targets for prevention as supplements to chronic hot spots. A combination program targeting both chronic and temporary hot spots is recommended