Application of Multivariate Modelling to Global Warming Projections

Abstract

Global warming is a natural phenomenon in which the accumulation of energy in the Earth's atmospheric system leads to an increase in temperature. To build the model, we collected multiple datasets from the 20th century: average annual global temperatures, average monthly temperatures at different latitudes, global losses from various natural disasters, global emissions of various chemicals, and population. We then pre-processed and visualized the data. In our quantitative analysis, we found that the increase in global temperature in March 2022 did not exceed the increase in the past 10 years. We then developed three models to describe past and project future global temperature levels: the GM(1,1) model, the ARIMA(0,1,1) model, and the Holt model. By primarily comparing the fit of the three models, we concluded that the Holt model has the highest prediction accuracy. We found that the relationship between time and global temperature.