Author(s)

Maryam I. Habadi^*, Chris P. Tsokos

Department of Mathematics and Statistics, University of South Florida, Tampa, USA.

Time series models are very powerful methods that help to drive hidden visions of a phenomenon and make informed future decisions. The purpose of this study is to develop statistical time series forecasting models to predict atmospheric carbon dioxide concentration in the Middle East and temperature in Saudi Arabia using multiplicative seasonal autoregressive integrated moving average models. We proceed to verify the quality and usefulness of our proposed probabilistic models by utilizing essential statistical properties to evaluate them according to their performance in forecasting the carbon dioxide in the atmosphere and the corresponding temperatures and it was shown that both statistical forecasting models produced good estimates.

Non-Stationary Time Series, ARIMA, Global Warming

Habadi, M. and Tsokos, C. (2017) Statistical Forecasting Models of Atmospheric Carbon Dioxide and Temperature in the Middle East. Journal of Geoscience and Environment Protection, 5, 11-21. doi: 10.4236/gep.2017.510002.