Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences

Theoretical and Natural Science

Vol. 30, 24 January 2024

Open Access | Article

U.S. unemployment rate prediction using time series model

Xintian Zou * 1
1 China University of Petroleum-Beijing at Karamay

* Author to whom correspondence should be addressed.

Advances in Humanities Research, Vol. 30, 255-262
Published 24 January 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Xintian Zou. U.S. unemployment rate prediction using time series model. TNS (2024) Vol. 30: 255-262. DOI: 10.54254/2753-8818/30/20241129.


Although previous studies have given a better prediction model for the American’s unemployment rate, due to the short time and different time nodes, the parameters of the model and the seasonality and the stability of the time series are also different. In this study, the ARIMA model, which is the most widely used in the time series, is adopted and the seasonal influence is added to the model according to the selected time period. At the same time, two models are used to predict the unemployment rate in the United States from January 2017 to January 2019. The stability of the model was determined by Dickey-Fuller test, and the fitting and prediction effects of the two models were compared by comparing the values of AIC and MSE. With the fitting prediction method of the unemployment rate in the United States, this paper can analyze and predict the unemployment rate in other Western countries, and can further compare and analyze the reasons with China ‘s unemployment rate, which is convenient for us to better regulate macroeconomic policies.


Unemployment rate, time series analysis, ARIMA, SARIMA, unit root test


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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
ISBN (Print)
ISBN (Online)
Published Date
24 January 2024
Theoretical and Natural Science
ISSN (Print)
ISSN (Online)
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated