A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA

Ulke V., Sahin A., Subasi A.

NEURAL COMPUTING & APPLICATIONS, vol.30, no.5, pp.1519-1527, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 5
  • Publication Date: 2018
  • Doi Number: 10.1007/s00521-016-2766-x
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1519-1527
  • Keywords: Inflation forecasting, Time series models, Machine learning models, SUPPORT VECTOR REGRESSION, ARTIFICIAL NEURAL-NETWORKS, TECHNICAL ANALYSIS, EXCHANGE-RATES, GARCH MODEL, RANDOM-WALK, COMBINATION, VOLATILITY, INDEX
  • Gazi University Affiliated: Yes

Abstract

This study compares time series and machine learning models for inflation forecasting. Empirical evidence from the USA between 1984 and 2014 suggests that out of sixteen conditions (four different inflation indicators and four different horizons), machine learning models provide more accurate forecasting results in seven conditions and the time series models are better in nine conditions. Moreover, multivariate models give better results in fourteen conditions, and univariate models are better only in two conditions. This study shows that machine learning model prevails against time series models for the core personal consumption expenditure (core-PCE) inflation forecasting, and the time series model (ARDL) is better for the core consumer price (core-CPI) index inflation forecasting in all horizons.