TITLE:
Measuring Global Warming: Global and Hemisphere Mean Temperature Anomalies Predictions Using Sliced Functional Time Series (SFTS) Model
AUTHORS:
Farah Yasmeen
KEYWORDS:
Temperature Series, Hemispheric Temperature, Temperature Anomalies, Global Warming, Weather Prediction, Sliced Functional Time Series, Outliers
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.9 No.5,
May
6,
2019
ABSTRACT: In this study, the sliced functional time series (SFTS) model is applied
to the Global, Northern and Southern temperature anomalies. We obtained the
combined land-surface air and sea-surface water temperature from Goddard
Institute for Space Studies (GISS), NASA. The data are available for Global mean, Northern Hemisphere mean and Southern
Hemisphere 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 of
global warming. We detected the outliers during the last century not only in
global temperature series but also in northern and southern hemisphere series.
The forecasts for the next twenty years are obtained using SFTS models. These
forecasts are compared with ARIMA, Random Walk with drift and Exponential
Smoothing State Space (ETS) models. The comparison is made on the basis of root
mean square error (RMSE), mean absolute percentage error (MAPE) and the length
of prediction intervals.