Neuro Fuzzy Modelling for Prediction of Consumer Price Index

TL;DR

This study develops a neuro fuzzy machine-learning model to predict the Consumer Price Index using Tanzanian data, achieving higher accuracy than previous models with specific architecture and error metrics.

Contribution

It introduces a neuro fuzzy approach for CPI prediction, demonstrating improved accuracy over existing models with a detailed architecture and evaluation on Tanzanian data.

Findings

Neuro fuzzy model architecture 5:74:1 with Gaussian membership functions.

RMSE of 0.44886 and MAPE of 0.23384 achieved.

Model outperforms previous CPI prediction studies.

Abstract

Economic indicators such as Consumer Price Index (CPI) have frequently used in predicting future economic wealth for financial policy makers of respective country. Most central banks, on guidelines of research studies, have recently adopted an inflation targeting monetary policy regime, which accounts for high requirement for effective prediction model of consumer price index. However, prediction accuracy by numerous studies is still low, which raises a need for improvement. This manuscript presents findings of study that use neuro fuzzy technique to design a machine-learning model that train and test data to predict a univariate time series CPI. The study establishes a matrix of monthly CPI data from secondary data source of Tanzania National Bureau of Statistics from January 2000 to December 2015 as case study and thereafter conducted simulation experiments on MATLAB whereby ninety five percent (95%) of data used to train the model and five percent (5%) for testing. Furthermore, the study use root mean square error (RMSE) and mean absolute percentage error (MAPE) as error metrics for model evaluation. The results show that the neuro fuzzy model have an architecture of 5:74:1 with Gaussian membership functions (2, 2, 2, 2, 2), provides RMSE of 0.44886 and MAPE 0.23384, which is far better compared to existing research studies.