Time-series modeling and prediction of global monthly absolute temperature for environmental decision making
By Liming Ye, Guixia Yang, E. Ranst, Huajun Tang. Advances in Atmospheric Sciences, 2013
59 citations recorded by the source corpus.
A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (∼10-year) environmental planning and decision making.
Subfields: Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/c2afd421895d34b4d735aa678e85e205c00c4f5f
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In Global and Planetary Change
Citations in the corpus
- Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA (2019, background): “Furthermore, the results of other studies demonstrated the satisfactory performance of time series model for predicting temperature in other applications (Ye et al. 2013; Asamoah-Boaheng 2014; John et al. 2014; Musa 2014).”