Prediction of Consumer Price Index for Industrial Workers (CPIIW) Using Machine Learning Approaches: Evidence from India
Author : BS Kambo, Gurinder Singh and Jassimar Singh
Abstract :
This study fitted the consumer price index for industrial workers (CPIIW) in India using a time series data on Consumer Price Index for Industrial Workers. A comparative evaluation of 28 Machine learning Models for time series data has been carried out. An invariant of Recurrent Neural Network Model viz. Long Short-Term Memory Model (LSTM) was found to be best fit to the CPIIW data. The Performance of the Models were assessed by the four matrices: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) and R Square (R2). It offered a two-year projection for the anticipated CPIIW. The CPIIW's two-year forecast, which runs from January 2024 to December 2025, was finally made. According to the study, the CPIIW will increase by about 3.4 points during the summer/rainy season (April to September) and stay relatively stable during the winter (October to March).
Keywords :
LSTM, CNN, RNN, DEEP LEARNING, RMSR, MAPE, MAE, R2
