Standard

Forecasting Recessions in the US Economy Using Machine Learning Methods. / Zyatkov, Nikolay; Krivorotko, Olga.

Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 139-146 (Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Zyatkov, N & Krivorotko, O 2021, Forecasting Recessions in the US Economy Using Machine Learning Methods. in Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021. Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021, Institute of Electrical and Electronics Engineers Inc., pp. 139-146, 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021, Moscow, Russian Federation, 13.09.2021. https://doi.org/10.1109/OPCS53376.2021.9588678

APA

Zyatkov, N., & Krivorotko, O. (2021). Forecasting Recessions in the US Economy Using Machine Learning Methods. In Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021 (pp. 139-146). (Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/OPCS53376.2021.9588678

Vancouver

Zyatkov N, Krivorotko O. Forecasting Recessions in the US Economy Using Machine Learning Methods. In Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 139-146. (Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021). doi: 10.1109/OPCS53376.2021.9588678

Author

Zyatkov, Nikolay ; Krivorotko, Olga. / Forecasting Recessions in the US Economy Using Machine Learning Methods. Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 139-146 (Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021).

BibTeX

@inproceedings{f4ffe115b69d4e0b91191c714cc77004,
title = "Forecasting Recessions in the US Economy Using Machine Learning Methods",
abstract = "A quantitative analysis of socio-economic characteristics, the set of which is typical in the pre-crisis periods of a market economy, is carried out. An indicator for forecasting the onset of a recession in the US economy over the next 6, 12 and 24 months has been constructed using machine learning methods (k-nearest neighbors, support vector machine, fully connected neural network, LSTM neural network, etc.). Using roll forward cross-validation, it is shown that the smallest error in predicting the onset of future recessions was obtained by a fully connected neural network. It is also shown that all three constructed indicators successfully predict the onset of each of the last six recessions that occurred in the United States from 1976 to 2021 (Early 1980s recession, Recession of 1981-82, Early 1990s recession,.COM bubble recession, Great Recession, COVID-19 recession). The resulting indicators can be used to assess future economic activity in the United States using current macroeconomic indicators.",
keywords = "deep learning, financial crisis, machine learning, recession, socio-economic processes, US economy",
author = "Nikolay Zyatkov and Olga Krivorotko",
note = "Funding Information: This work is supported by the Russian Science Foundation (project no. 18-71-10044). Publisher Copyright: {\textcopyright} 2021 IEEE; 17th International Asian School-Seminar {"}Optimization Problems of Complex Systems{"}, OPCS 2021 ; Conference date: 13-09-2021 Through 17-09-2021",
year = "2021",
doi = "10.1109/OPCS53376.2021.9588678",
language = "English",
isbn = "978-1-6654-0563-8",
series = "Proceedings - 2021 17th International Asian School-Seminar {"}Optimization Problems of Complex Systems{"}, OPCS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "139--146",
booktitle = "Proceedings - 2021 17th International Asian School-Seminar {"}Optimization Problems of Complex Systems{"}, OPCS 2021",
address = "United States",

}

RIS

TY - GEN

T1 - Forecasting Recessions in the US Economy Using Machine Learning Methods

AU - Zyatkov, Nikolay

AU - Krivorotko, Olga

N1 - Funding Information: This work is supported by the Russian Science Foundation (project no. 18-71-10044). Publisher Copyright: © 2021 IEEE

PY - 2021

Y1 - 2021

N2 - A quantitative analysis of socio-economic characteristics, the set of which is typical in the pre-crisis periods of a market economy, is carried out. An indicator for forecasting the onset of a recession in the US economy over the next 6, 12 and 24 months has been constructed using machine learning methods (k-nearest neighbors, support vector machine, fully connected neural network, LSTM neural network, etc.). Using roll forward cross-validation, it is shown that the smallest error in predicting the onset of future recessions was obtained by a fully connected neural network. It is also shown that all three constructed indicators successfully predict the onset of each of the last six recessions that occurred in the United States from 1976 to 2021 (Early 1980s recession, Recession of 1981-82, Early 1990s recession,.COM bubble recession, Great Recession, COVID-19 recession). The resulting indicators can be used to assess future economic activity in the United States using current macroeconomic indicators.

AB - A quantitative analysis of socio-economic characteristics, the set of which is typical in the pre-crisis periods of a market economy, is carried out. An indicator for forecasting the onset of a recession in the US economy over the next 6, 12 and 24 months has been constructed using machine learning methods (k-nearest neighbors, support vector machine, fully connected neural network, LSTM neural network, etc.). Using roll forward cross-validation, it is shown that the smallest error in predicting the onset of future recessions was obtained by a fully connected neural network. It is also shown that all three constructed indicators successfully predict the onset of each of the last six recessions that occurred in the United States from 1976 to 2021 (Early 1980s recession, Recession of 1981-82, Early 1990s recession,.COM bubble recession, Great Recession, COVID-19 recession). The resulting indicators can be used to assess future economic activity in the United States using current macroeconomic indicators.

KW - deep learning

KW - financial crisis

KW - machine learning

KW - recession

KW - socio-economic processes

KW - US economy

UR - http://www.scopus.com/inward/record.url?scp=85126976193&partnerID=8YFLogxK

U2 - 10.1109/OPCS53376.2021.9588678

DO - 10.1109/OPCS53376.2021.9588678

M3 - Conference contribution

AN - SCOPUS:85126976193

SN - 978-1-6654-0563-8

T3 - Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021

SP - 139

EP - 146

BT - Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021

Y2 - 13 September 2021 through 17 September 2021

ER -

ID: 35854026