Survey on During Pandemic scenario: Domestic Retail Sales Forecasting of Passenger Vehicles in India using Time Delay Neural Network
Main Article Content
Abstract
Accurate sales forecast is crucial to plan the production process, especially in automotive
industry, where a large number of factors –macro-economic, micro-economic and others
dynamically influence the demand.Forecasting in such a dynamic industry, especially with the
onset of COVID, has become extremely important with the preference for personal
transportation on the increase. In this paper, a neural network model to forecast the domestic
retail sales of passenger vehicles in India is formulated. A Time Delay Feed Forward Neural
Network with Back Propagation (TDNN)is used and the forecasting accuracies determined by
comparing the values of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean
Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The macroeconomic
indicators identified were Unemployment rate, GDP growth rate, Private Final
Consumption Expenditure (PFCE), Index of Industrial Production (IIP), Bank Lending Rate
(BLR), Inflation Rate and Crude oil prices. The indicators were taken as the input variables and
Passenger Vehicle Retail Sales was selected as the target variable to formulate the TDNN
model. It was observed that the TDNN model was able to forecast the output with great
accuracy.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
