Survey on During Pandemic scenario: Domestic Retail Sales Forecasting of Passenger Vehicles in India using Time Delay Neural Network (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
Accurate sales forecast is crucial to plan the production process, especially in automotiveindustry, where a large number of factors –macro-economic, micro-economic and othersdynamically influence the demand.Forecasting in such a dynamic industry, especially with theonset of COVID, has become extremely important with the preference for personaltransportation on the increase. In this paper, a neural network model to forecast the domesticretail sales of passenger vehicles in India is formulated. A Time Delay Feed Forward NeuralNetwork with Back Propagation (TDNN)is used and the forecasting accuracies determined bycomparing the values of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), MeanAbsolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The macroeconomicindicators identified were Unemployment rate, GDP growth rate, Private FinalConsumption 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 andPassenger Vehicle Retail Sales was selected as the target variable to formulate the TDNNmodel. It was observed that the TDNN model was able to forecast the output with greataccuracy.