• Piyush Jain
• Parv Arora

Automotive sector is one of the most crucial sectors for any developed or developing Economy. Unknown Demand for vehicles is a significant problem that decreases the productivity of the production environment. Thus, forecasting plays a vital role in planning the production and inventory of an industry. Time series forecasting is a type of forecasting in which last period data is used to determine the industry's trends and sales in the future. In this paper, the authors have deployed nine time series forecasting models for the Demand for vehicles in the Indian Automotive Sector. Authors have deployed Naïve Method, Simple Moving Average, Weighted Moving Average, Simple Linear Regression, Exponential Smoothening, Holt Winter linear Trend, Autoregression, Autoregression Moving Average, and Autoregression Integrated Moving Average models to compare the forecasted Demand and find the best model based on MAPE (Mean Absolute Percentage Error) value. This paper aims to scrutinize these nine models' ability to be implemented while performing market analysis or production planning of automobiles. The result of models helps analyze which model has greater accuracy and less percentage error, which will help production units.

Demand Forecasting, Forecasting, ARIMA, Auto Regression, Moving Average, ARMA, Holt Winter

"Analysis of Time Series Forecasting Techniques for Indian Automotive Industry", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.d831-d840, July-2021, Available :http://www.jetir.org/papers/JETIR2107494.pdf

"Analysis of Time Series Forecasting Techniques for Indian Automotive Industry", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppd831-d840, July-2021, Available at : http://www.jetir.org/papers/JETIR2107494.pdf