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Measuring Global Warming: Global and Hemisphere Mean Temperature Anomalies Predictions Using Sliced Functional Time Series (SFTS) Model

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Document pages: 19 pages

Abstract: In this study, the sliced functional time series (SFTS) model is appliedto the Global, Northern and Southern temperature anomalies. We obtained thecombined land-surface air and sea-surface water temperature from GoddardInstitute for Space Studies (GISS), NASA. The data are available for Global mean, Northern Hemisphere mean and SouthernHemisphere means (monthly, quarterly and annual) since 1880 to present(updated through March 2019). We analyze the global surface temperature change,compare alternative analyses, and address the questions about the reality ofglobal warming. We detected the outliers during the last century not only inglobal temperature series but also in northern and southern hemisphere series.The forecasts for the next twenty years are obtained using SFTS models. Theseforecasts are compared with ARIMA, Random Walk with drift and ExponentialSmoothing State Space (ETS) models. The comparison is made on the basis of rootmean square error (RMSE), mean absolute percentage error (MAPE) and the lengthof prediction intervals.

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