Abstract

Identifying factors having influential impact on automobile demand estimation has become a primary concern for automobile industry. There has been substantial development modeling for automobile demand estimation. The Regression, Time series, CHAID and RBF neural network modeling types are proposed. In this study, automobile demand is classified into three classes. The first issue to be addressed by this study is to selecting the most appropriate modeling types in each class by comparing the actual demand and estimated demand in terms of Root-Mean-Square-Error (RMSE). Results indicate that modeling can be different for different classes of automobile demand. The second aim of this study is to identify the significant indicators for estimating automobile demand in each class of Iranian market.

Keywords:

CHAID method, RBF neural network, regression method, RMSE, time series method,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.