Applying Machine Learning to Predict the Consumer Price Index in Saudi Arabia
DOI:
https://doi.org/10.14738/aivp.115.15556Keywords:
Machine Learning, Boosting, Prediction Algorithms, Consumer Price Index, Economic ImpactAbstract
One of the key economic indicators is the consumer price index (CPI) which has frequently been used by financial policy makers of several countries to predict inflation (or deflation). Using guidelines from various studies, a majority of central banks have began adopting monetary policies targeted as inflation which call for a stringent requirement of an accurate prediction model of the CPI. However, the prediction accuracy achieved by numerous studies is low and must be improved. This study presents an attempt to forecast the future CPI of Saudi Arabia based on the previous years' CPI data. It develops a matrix comprising monthly CPI data derived from the General Authority for Statistics covering the period of January 2002 to December 2018 to be used as a case study and subsequently carries out simulations in WEKA, where seventy percent (70%) of the data is used to train the model and thirty percent (30%) is used for testing. Furthermore, the study uses the root mean squared error (RMSE), with average error rate of 0.436, the mean absolute error (MAE), with average error rate of 0.281, the relative absolute error (RAE), with an average error rate of 8.963, the root relative squared error (RRSE), with an average error rate of 11.653, and the correlation coefficient (CC), with an average error rate of 0.987, as error metrics for a performance evaluation. The second part of the study is conducted using simulations in Mathematica and CPI data of Saudi Arabia from 1963 to 2019 to predict yearly future CPI using the gradient boosted trees prediction algorithm, which provides sufficiently accurate predictions. The gradient boosted trees prediction is also compared with other prediction algorithms.
References
M. Friedman, “The lag in effect of monetary policy,” Journal of Political Economy, vol. 69, pp. 447–466, 1961.
D. Ida, “Sectoral inflation persistence and optimal monetary policy,” Journal of Macroeconomics, vol. 65, article id. 103215, 2020.
General Authority for Statistics, Kingdom of Saudi Arabia, 2021. [Online]. Available: https://www.stats.gov.sa/en/394
A. Almosova and N. Andresen, “Nonlinear inflation forecasting with recurrent neural networks,” Technical Report: European Central Bank (ECB), vol. 2019, pp. 1–45, 2019.
D. Aparicio and M. Bertolotto, “Forecasting inflation with online prices,” International Journal of Forecasting, vol. 36, pp. 232–247, 2020.
S. Athey, “The impact of machine learning on economics,” in A. Agrawal, J. Gans and A. Goldfarb, The Economics of Artificial Intelligence: An Agenda, 2nd ed., Chicago, IL, USA: University of Chicago Press, pp. 507–547, 2019.
A. Atkeson and E. Ohanian, “Are Phillips curves useful for forecasting inflation?” Federal Reserve Bank of Minneapolis Quarterly Review, vol. 25, pp. 2–11, 2001.
B. Bernanke, T. Laubach, F. Mishkin and A. Posen, Inflation Targeting: Lessons from the International st Experience, 1 ed., Princeton, NJ, USA: Princeton University Press, 1999.
C. Chakraborty and A. Joseph, “Machine learning at central banks,” Bank of England working papers, vol. 2017, working paper no. 674, pp. 1–89, 2017.
X. Chen, J. Racine and N. Swanson, “Semiparametric arx neural-network models with an application to forecasting inflation,” IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 674–683, 2001.
M. Choudhary, and A. Haider, “Neural network models for inflation forecasting: an appraisal,” Applied Economics, vol. 44, pp. 2631–2635, 2012.
S. Zahara, Sugianto and M. Ilmiddaviq, “Consumer price index prediction using Long Short-Term Memory (LSTM) based cloud computing,” Journal of Physics: Conference Series, vol. 1456, no. 012020, pp. 1–8, 2020.
C. Yang, S. Guo, “Inflation Prediction Method Based on Deep Learning,” Computational Intelligence and Neuroscience, vol. 2021, article id. 1071145, pp. 1–6, 2021.
D. Thu Huong, V. Truong and B. Lam, “Forecasting of consumer price index using the ensemble learning model with multi-objective evolutionary algorithms: Preliminary results,” in Proc. 2015 International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam, pp. 337–342, 2015. th
I. Witten, E. Frank, M. Hall and C. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4 ed., Burlington, MA, USA: Morgan Kauffman, 2016.
J. Friedman, “Stochastic gradient boosting,” Computational Statistics and Data Analysis, vol. 38, no. 4, pp. 367–378, 2002. URL: http://dx.doi.org/10.14738/aivp.115.15556 119
N. Woloszko, “Adaptive Trees: a new approach to economic forecasting,” in OECD Economics Department st Working Papers No. 1593, 1 ed., Paris, France: OECD Publishing, 2020.
J. Mohamed, “Time series modeling and forecasting of Somaliland consumer price index: A comparison of ARIMA and regression with ARIMA errors,” American Journal of Theoretical and Applied Statistics, vol. 9, no. 4, pp. 143–153, 2020.
I. Purbasari, F. Anggraeny and N. Ardiningrum, “Time-series modeling for consumer price index forecasting using comparison analysis of autoregressive integrated moving average and artificial neural network,” in Proc. International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies (CESIT), Medan, Indonesia, pp. 599–604, 2020.
D. Santos Júnior, J. Oliveira, P. Neto, “An intelligent hybridization of ARIMA with machine learning models for time series forecasting,” Knowledge-Based Systems, vol. 175 pp. 72–78, 2019.
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