Application of time series models for forecasting the global temperature anomalies

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,whichcan be used to predict the values of theseries. Three models of theseries 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.