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(downloads: 55)
Language:
Russian
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Article type:
Article
UDC:
551.5
EDN:
LPKOEH
Application of time series models for forecasting the global temperature anomalies
Autors:
Bogdanov Mikhail Borisovich, Saratov State University
Morozova Svetlana Vladimirovna, Saratov State University
Alimpieva Mariya A., Saratov State University
Abstract:
Spectral analysis of the time series for average annual values of the globally averaged surface temperature anomaly shows the presence of harmonics of the lunar nodal cycle with a period of 18.6 years, which can be used to predict the values of the series. Three models of the series were considered: autoregression AR(p), combined model of autoregression – integrated moving average ARIMA(p,d,q) and artificial neural network. It is shown that the ARIMA(4,1,4) model gives the best results for predicting the global temperature anomaly for three years.
Key words:
Reference:
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Received:
15.08.2022
Accepted:
01.09.2022
Published:
23.12.2022