The prediction and analysis of global climate change based on SARIMA

Abstract

Global climate change is a significant challenge that the world is currently facing. Accurate prediction of global climate change is essential for environmental protection, agricultural production, and social development. This study explores the utilization of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model for forecasting global climate change. The SARIMA model is a machine learning algorithm that can effectively capture seasonal patterns and non-linear characteristics of climate data. The study initiates by performing data preprocessing tasks, which encompass data cleaning, managing missing values, and converting the data into a suitable format for analysis. The SARIMA model is then constructed, considering the seasonality and autocorrelation of the climate data. Historical climate data is used to train the SARIMA models, which are then utilized to forecast future global climate changes. The predictive performance of the models is evaluated to validate the effectiveness and accuracy of the SARIMA model in global climate change prediction. Experimental results indicate that the SARIMA model effectively captures the underlying patterns and dynamics of the climate data. The accurate predictions of the SARIMA model have practical implications for understanding and forecasting global climate change. These forecasts provide insightful information for policy formulation and decision-making, aiding in the development of innovative strategies to mitigate and adapt to climate change.