Development and evaluation of probabilistic forecasting methods for small area populations

• Published in: Environment and planning. B, Urban analytics and city science Vol. 51; no. 2; pp. 366 - 383
• Main Authors: Grossman, Irina, Bandara, Kasun, Wilson, Tom, Kirley, Michael
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-02-2024
• Subjects: growth and change analysis; Uncertainty; urban planning; urban analytics; demographic change
• ISSN: 2399-8083; 2399-8091
• DOI: 10.1177/23998083231178817

Planning and development decisions in both the government and business sectors often require small area population forecasts. Unfortunately, current methods often produce forecasts that are inaccurate, particularly for remote areas and those with smaller populations. Such inaccuracy necessitates the development and evaluation of methods to forecast and communicate forecast uncertainty, however, little research has been conducted in this domain for small area populations. In this paper, we evaluate a set of probabilistic forecasting methods which include Autoregressive integrated moving average, Exponential Smoothing, THETA, LightGBM and XGBOOST, to produce point forecasts and 80% prediction intervals for Australian SA2 small area populations. We also investigate methods to combine the intervals to produce ensemble forecasts. Our results show that individual probabilistic methods generally produce prediction intervals which underestimate forecast uncertainty. Combining forecasts improves the overall accuracy of point forecasts and the coverage of their intervals, however, coverage still tends to be less than the expected 80% for all but the most conservative combination method.