Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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. стр. 139-146 (Proceedings - 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
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