GRU-RNN Model to Analyze and Predict the Inflation by Consumer Price Index

Abstract

The general population need data on the expected rate of inflation for educational expenses to plan for and manage family budgets. When formulating educational policy, this data is also useful for the government. Chennai serves as one of Indonesia's educational hubs, yet there is a lack of publicly accessible data on the city's inflation rate for education expenses. Furthermore, there is a lack of prior studies on forecasting that specifically used the Education Expenditure Group's Consumer Price Index (CPI) information to determine inflation rates in education prices utilizing the Deep Learning approach. The goal of this study is to utilize the Deep Learning Method to create a model that can predict how much tuition will rise in Chennai. The Education Expenditure Group in Chennai relied on Consumer Price Index (CPI) information for its study. An improvement in the Long and Short-Term Memory (LSTM) and Recurrent Neural Network (RNN), approach is used for predicting purposes. A method with four hidden nodes and a single hidden layer produced the best results, with RMSE=8.38 and MAPE=2.8766%.