Global Warming: Predictive Models and Correlation Analysis

Abstract

In recent years, the Earth's rising temperatures and the shrinking Antarctic ice cover have become increasingly evident. Understanding the factors driving global warming and developing strategies to mitigate it are critical. By applying the Pettitt change-point detection method, we analyzed March temperatures from the past decade and identified a significant shift occurring in March 2015. Forecasting models, including ARIMA and LSTM, predict similar temperature trends, with the LSTM model demonstrating slightly higher accuracy. Through grey correlation analysis and multiple linear regression, we identified CO2 as the most influential factor affecting global temperatures. Therefore, reducing CO2 emissions is crucial to mitigating global warming.