Improving forecasts for noisy geographic time series

Samuel H. Huddleston, John H. Porter and Donald E. Brown

Journal of Business Research, 2015, vol. 68, issue 8, 1810-1818

Abstract: 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.

Keywords: Holt–Winters; ARIMA; Top-down forecasting; Crime forecasting; Geographic time series (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:68:y:2015:i:8:p:1810-1818

DOI: 10.1016/j.jbusres.2015.03.040

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