Improving forecasts for noisy geographic time series

This study compares the performance of several simple top-down forecasting methods for forecasting noisy geographic time series to the performance of the three methods most commonly used for this problem: naive methods, Holt-Winters (exponential) smoothing, and the ARIMA (Box-Jenkins) class of models. The problem of producing weekly burglary forecasts at the precinct and patrol sector level in the city of Pittsburgh over a five-year period provides a case study for performance comparison. All top-down forecasting methods improve forecasting performance while significantly reducing the modeling workload. These results suggest that simple top-down forecasting models may provide a general-purpose method for improving forecasting for noisy geographic time series in many applications.