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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

verfasst von: Volkan Ülke, Afsin Sahin, Abdulhamit Subasi

Erschienen in: Neural Computing and Applications | Ausgabe 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.

Vorheriger Artikel Optimization and predictive modeling using S/N, RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel

Nächster Artikel Multi-criteria decision-making method based on single-valued neutrosophic linguistic Maclaurin symmetric mean operators

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Titel: A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA
verfasst von: Volkan Ülke
Afsin Sahin
Abdulhamit Subasi
Publikationsdatum: 17.12.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2766-x

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