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Improving forecasts for noisy geographic time series

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

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.

Suggested Citation

  • Huddleston, Samuel H. & Porter, John H. & Brown, Donald E., 2015. "Improving forecasts for noisy geographic time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1810-1818.
    • Handle: RePEc:eee:jbrese:v:68:y:2015:i:8:p:1810-1818
    DOI: 10.1016/j.jbusres.2015.03.040
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    1. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    2. Usman Ghani & Peter Toth & Fekete David, 2023. "Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries," Sustainability, MDPI, vol. 15(10), pages 1-15, May.

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