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Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions

Author

Listed:
  • Roberto Patuelli
  • Peter Nijkamp
  • Simonetta Longhi

    (Institute for Social and Economic Research, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, England)

  • Aura Reggiani

    (Department of Economics, Faculty of Statistics, University of Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy)

Abstract

This paper develops and applies neural network (NN) models to forecast regional employment patterns in Germany. Computer-aided optimization tools that imitate natural biological evolution to find the solution that best fits the given case (namely, genetic algorithms, GAs) are also used to detect the best NN structure. GA techniques are compared with more ‘traditional’ techniques which require the supervision of experienced analysts. We test the performance of these techniques on a panel of 439 districts in West and East Germany. Since the West and East datasets have different time spans, the models are estimated separately for West and East Germany. The results show that the West and East NN models perform with different degrees of precision, mainly because of the different time spans of the two datasets. Automatic techniques for the choice of the NN architecture do not seem to outperform selection procedures based on the supervision of expert analysts.

Suggested Citation

  • Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    • Handle: RePEc:sae:envirb:v:35:y:2008:i:4:p:701-722
    DOI: 10.1068/b3101
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    References listed on IDEAS

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    1. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
    2. Wozniak Marcin, 2020. "Forecasting the unemployment rate over districts with the use of distinct methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-20, April.
    3. Ferenc Bakó & Judit Berkes & Cecília Szigeti, 2021. "Households’ Electricity Consumption in Hungarian Urban Areas," Energies, MDPI, vol. 14(10), pages 1-23, May.
    4. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Advances in Spatial Science, in: Esteban Fernández Vázquez & Fernando Rubiera Morollón (ed.), Defining the Spatial Scale in Modern Regional Analysis, edition 127, chapter 0, pages 173-192, Springer.
    5. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    6. repec:dgr:vuarem:2009-14 is not listed on IDEAS
    7. Zhou, You & Zhang, Lingzhu & Chiaradia, Alain J F, 2021. "An adaptation of reference class forecasting for the assessment of large-scale urban planning vision, a SEM-ANN approach to the case of Hong Kong Lantau tomorrow," Land Use Policy, Elsevier, vol. 109(C).

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    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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