Machine Learning Approach for the Prediction of Consumer Food Price Index

The price of food and food related items are dynamic. A measure change in the price affects the buying behaviour of the consumer and monetary policies by the Government. The Consumer Food Price Index (CFPI) reflects the variations in food prices during a certain period. In India, the CFPI is released monthly by the Central Statistical Organization. It also reflects the inflation and helps the Government to take corrective measures in time. In this paper we have applied the machine learning approach in forecasting the consumer food price index in India. In specific, this work has focused on the applicability of Artificial Neural Network (ANN) models with back propagation learning in predicting the future values of CFPI. The monthly data for rural, urban and combined from the period 2013 to 2021 have been used to train and validate the models. The Mean Absolute Percentage Error (MAPE) values have been used to validate the accuracy of the models. The experimental results show that a simple ANN model with back propagation algorithm is highly capable in forecasting the future values of CFPI.