A Comparative Study On Forecasting Consumer Price Index Of India Amongst XGBoost, Theta, ARIMA, Prophet And LSTM Algorithms

CPI often referred to as the Consumer Price Index is a crucial and thorough method employed to estimate price changes over a fixed time interval within a country which is representative of consumption expenditure in a country‘s economy. CPI being an economic indicator engenders therefore the popular metric called inflation of the country. Thus, if we can accurately forecast the CPI, the country‘s economy can be controlled well in time and appropriate decision-making can be enabled. Hence, for a decade CPI index forecasting, especially in a developing country like India, has been always a matter of interest and research topic for economists and policy of the government. To forecast CPI, humans (decision makers) required vast domain knowledge and experience. Moreover, traditional CPI forecasting involved a multitude of human interventions and discussions for the same. However, with the recent advancements in the domain of time series forecasting techniques encompassing dependable modern machine learning, statistical as well as deep learning models there exists a potential scope in leveraging modern technology to forecast CPI of India which can technically aid towards this important decision-making step in a diverse country like India. In this paper, a comparative study is carried out exploring MAD, RMSE, and MAPE as comparison criteria amongst Machine Learning (XGBoost), Statistical Learning (Theta, ARIMA, Prophet) and Deep Learning (LSTM) algorithms. Furthermore, from this comparative univariate time series forecasting study, it can be demonstrated that technological solutions in the domain of forecasting show promising results with reasonable forecast accuracy.