DOI : https://doi.org/10.18517/ijaseit.10.6.8475

An Integrated Model for Forecasting Indian Automobile

Kayalvizhi Subramanian (1), Mohammad Othman (2), Rajalingam Sokkalingam (3), Gunasekar Thangarasu (4), S. Kayalvizhi (5)
(1) Faculty of Fundamental Science, University Technology PETRONAS , Perak, Malaysia
(2) Faculty of Fundamental Science, University Technology PETRONAS , Perak, Malaysia
(3) Faculty of Fundamental Science, University Technology PETRONAS , Perak, Malaysia
(4) Department of Professional, Industry Driven Education (PRIDE), Mahsa University, Malaysia
(5) Faculty of Built Environment, Linton University College, Malaysia
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How to cite (IJASEIT) :
Subramanian, Kayalvizhi, et al. “An Integrated Model for Forecasting Indian Automobile”. International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, Dec. 2020, pp. 2593-8, doi:10.18517/ijaseit.10.6.8475.
Citation Format :
The automobile industry is one of India’s main economic sectors. In recent decades India has attracted many global players in the automobile industry. The industry has significantly benefited from an increase in the paying capacity of the consumers. This has contributed to increased competition in the market. Given that the automobile industry is a very complex process, a tool to predict the future of automotive demand from the modeling point of view is needed because of its high level of complexity and uncertainty. This study aims to introduce a novel integrated model with a combination of Adaptive Multiplicative Triple Exponential Smoothing Holt-Winters (AHW) method and Backpropagation Neural Networks (BPNNs) to improve the likelihood of predicting automobile sales accurately. This study is subject to continue validating a model in real-world automobile selling data against existing methods. This model also incorporates the linear and non-linear characteristics of AHW and BPNN, respectively to form a synergistic model.  The proposed model has the higher capability to provide reasonable accuracy in forecasting future sales in terms of average prediction accuracy of 0.974637 than the existing methods namely BPNN 0.9483 and ANN 0.9275. For training and testing purposes, validation is done using the Indian automobile sales data. Finally, the regression fit shows that during the test stage in the car sales data for the period 2016-2017 and 2017-2018, the proposed integrated model is better than the conventional method.

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