Forecasting automobile sales in Turkey with artificial neural networks

Aycan Kaya, Gizem Kaya, Ferhan Çebi

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


This study aims to reveal significant factors which affect automobile sales and estimate the automobile sales in Turkey by using Artificial Neural Network (ANN), ARIMA, and time series decomposition techniques. The forecasting model includes automobile sales, automobile price, Euro and Dollar exchange rate, employment rate, consumer confidence index, oil prices and industrial production confidence index, the probability of buying an automobile, female employment rate, general economic situation, the expectation of general economic situation, financial status of households, expectation of financial status of households. According to the regression results, changes in Dollar exchange rate, the expectation of financial status of households, seasonally adjusted industrial production index, logarithmic form of automobile sales before-one-month which have a significant effect on automobile sales, are found to be the significant variables. The results show that ANN has a better estimation performance with MAPE=1.18% and RMSE=782 values than ARIMA and time series decomposition techniques.

Original languageEnglish
Pages (from-to)50-60
Number of pages11
JournalInternational Journal of Business Analytics
Issue number4
Publication statusPublished - 1 Oct 2019

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  • Artificial Neural Network
  • Automotive
  • Demand Forecasting
  • Regression


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