The consumer price index prediction using machine learning approaches: Evidence from the United States

Tien-Thinh Nguyen; Hong-Giang Nguyen; Jen-Yao Lee; Yu-Lin Wang; Chien-Shu Tsai

doi:10.1016/j.heliyon.2023.e20730

The consumer price index prediction using machine learning approaches: Evidence from the United States

Heliyon. 2023 Oct 5;9(10):e20730. doi: 10.1016/j.heliyon.2023.e20730. eCollection 2023 Oct.

Authors

Tien-Thinh Nguyen¹, Hong-Giang Nguyen², Jen-Yao Lee¹, Yu-Lin Wang³, Chien-Shu Tsai⁴

Affiliations

¹ Department of International Business, National Kaohsiung University of Science and Technology, Kaohsiung City, 807618, Taiwan.
² Department of Academic and Students' Affairs, Hue University, Hue City, 49000, Viet Nam.
³ Department of Economics, National Chung Cheng University, Chiayi County, 621301, Taiwan.
⁴ Institute of Marine Affairs and Business Management, National Kaohsiung University of Science and Technology, Kaohsiung City, 811213, Taiwan.

Abstract

The consumer price index (CPI) is one of the most important macroeconomic indicators for determining inflation, and accurate predictions of CPI changes are important for a country's economic development. This study uses multivariate linear regression (MLR), support vector regression (SVR), autoregressive distributed lag (ARDL), and multivariate adaptive regression splines (MARS) to predict the CPI of the United States. Data from January 2017 to February 2022 were randomly selected and divided into two stages: 80 % for training and 20% for testing. The US CPI was modeled for the observed period and relied on a mix of elements, including crude oil price, world gold price, and federal fund effective rate. Evaluation metrics-mean absolute percentage value, mean absolute error, root mean square error, R-squared, and correlation of determination-were employed to estimate forecasted values. The MLR, SVR, ARDL, and MARS models attained high accuracy parameters, while the MARS algorithm generated higher accuracy in US CPI forecasts than the others in the testing phase. These outputs could support the US government in overseeing economic policies, sectors, and social security, thereby boosting national economic development.

Keywords: Autoregressive distributed lag model; Consumer price index; Forecasting; Multivariate adaptive regression splines model; Multivariate linear regression model; Support vector regression model.