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Neural Network Modeling and What-If Scenarios: Applications for Market Development Forecasting

In: Applications in Reliability and Statistical Computing

Author

Listed:
  • Valentina Kuskova

    (University of Notre Dame)

  • Dmitry Zaytsev

    (University of Notre Dame at Tantur)

  • Gregory Khvatsky

    (University of Notre Dame)

  • Anna Sokol

    (University of Notre Dame)

Abstract

In this chapter, we demonstrate the use of neural networks for forecasting automotive market sales. Using data from an entire country, with a large set of micro- and macroeconomic variables and other factors, we show how to supplement “black box” neural network calculations with a solid theoretical foundation from social sciences. Doing so allows to not only create exceptionally accurate forecasts, but understand the “black box” weights on different variables that are used in generating predictions. We also demonstrate how individual variables fit into the overall market dynamics, and how political changes play a role in economic outcomes.

Suggested Citation

  • Valentina Kuskova & Dmitry Zaytsev & Gregory Khvatsky & Anna Sokol, 2023. "Neural Network Modeling and What-If Scenarios: Applications for Market Development Forecasting," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Applications in Reliability and Statistical Computing, pages 271-288, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-21232-1_14
    DOI: 10.1007/978-3-031-21232-1_14
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