Prediction of Consumer Price Index (CPI) in Urban India using Statistical Methods

The Consumer Price Index (CPI) is an important economic indicator used to measure inflation and price changes in a basket of goods and services consumed in households. It is a crucial tool for policymakers, businesses, and individuals as it helps in policy formation, wage adjustment, investment decisions, etc. The Indian government collects information on the CPI of various categories of goods in rural and urban India. Food and beverages, cereals, milk, pulses, and their products take up a large portion of the overall CPI in Urban India. Therefore, predicting the CPI will help policymakers, businesses, and individuals make smarter choices. The work carried out in this paper focuses on predicting the CPI of food and beverages, cereals, milk, pulses, and their products in Urban India using statistical time series models such as ARIMA, SARIMA, and ETS. Univariate models were developed for each of the commodities and the best model was chosen based on least MAPE and RMSE values. The minimal MAPE and RMSE values obtained show that statistical time series models were successful in predicting the CPI of food and beverages, cereals, milk, pulses, and their products in Urban India.