Cite Article

An Integrated Model for Forecasting Indian Automobile

Choose citation format

BibTeX

@article{IJASEIT8475,
   author = {Kayalvizhi Subramanian and Mohammad Othman and Rajalingam Sokkalingam and Gunasekar Thangarasu and S. Kayalvizhi},
   title = {An Integrated Model for Forecasting Indian Automobile},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {6},
   year = {2020},
   pages = {2593--2598},
   keywords = {adaptive multiplicative exponential smoothing; AHW; backpropagation neural networks; automobile sales industry.},
   abstract = {

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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8475},    doi = {10.18517/ijaseit.10.6.8475} }

EndNote

%A Subramanian, Kayalvizhi
%A Othman, Mohammad
%A Sokkalingam, Rajalingam
%A Thangarasu, Gunasekar
%A Kayalvizhi, S.
%D 2020
%T An Integrated Model for Forecasting Indian Automobile
%B 2020
%9 adaptive multiplicative exponential smoothing; AHW; backpropagation neural networks; automobile sales industry.
%! An Integrated Model for Forecasting Indian Automobile
%K adaptive multiplicative exponential smoothing; AHW; backpropagation neural networks; automobile sales industry.
%X 

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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8475 %R doi:10.18517/ijaseit.10.6.8475 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 6 %@ 2088-5334

IEEE

Kayalvizhi Subramanian,Mohammad Othman,Rajalingam Sokkalingam,Gunasekar Thangarasu and S. Kayalvizhi,"An Integrated Model for Forecasting Indian Automobile," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2593-2598, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.6.8475.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Subramanian, Kayalvizhi
AU  - Othman, Mohammad
AU  - Sokkalingam, Rajalingam
AU  - Thangarasu, Gunasekar
AU  - Kayalvizhi, S.
PY  - 2020
TI  - An Integrated Model for Forecasting Indian Automobile
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 6
Y2  - 2020
SP  - 2593
EP  - 2598
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - adaptive multiplicative exponential smoothing; AHW; backpropagation neural networks; automobile sales industry.
N2  - 

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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8475 DO - 10.18517/ijaseit.10.6.8475

RefWorks

RT Journal Article
ID 8475
A1 Subramanian, Kayalvizhi
A1 Othman, Mohammad
A1 Sokkalingam, Rajalingam
A1 Thangarasu, Gunasekar
A1 Kayalvizhi, S.
T1 An Integrated Model for Forecasting Indian Automobile
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 6
YR 2020
SP 2593
OP 2598
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 adaptive multiplicative exponential smoothing; AHW; backpropagation neural networks; automobile sales industry.
AB 

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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8475 DO - 10.18517/ijaseit.10.6.8475