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Neural Computing and Applications

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17-12-2016 | Original Article

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

Authors: Volkan Ülke, Afsin Sahin, Abdulhamit Subasi

Published in: Neural Computing and Applications | Issue 5/2018

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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.

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Title: A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA
Authors: Volkan Ülke
Afsin Sahin
Abdulhamit Subasi
Publication date: 17-12-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 5/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2766-x

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