A comparative study of machine learning models and deep learning model for consumer price index of India

Abstract

In today's rapidly changing world, the significance of accurate and timely measures of the average change in prices of goods and services consumed by households over a specific period cannot be overstated. Accurate predictions of the Consumer Price Index are essential for various stakeholders, including policymakers, economists, and investors, as they rely on these predictions to make informed decisions and analyze economic trends. To improve the accuracy of price prediction models, researchers have turned to machine learning and deep learning techniques. Deep learning models, such as Long Short-Term Memory, have shown promising results in predicting the Consumer Price Index in a timely and accurate manner. These models are capable of incorporating both temporal and non-temporal variables, such as the inflation rate, which have an impact on price changes. Machine Learning models like Random Forest and support vector machines compare the analysis on the basis of Accuracy parameters and computation time.