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.

Identification of Probable Precipitation Formation Zones using Data Mining and Control Actions by Local Injections of Moisture Concentrators

“…Artificial intelligence methods serve as a promising direction for determining the probable precipitation formation zones, since neural networks, genetic algorithms and evolutionary calculations allow performing very accurate forecasts in meteorology in conditions of inaccuracy, uncertainty, and the need for approximation. To solve the problems of weather forecasting, the most effective and widespread method is the analysis of weather time series, the use of regression analysis [3][4][5].…”