Global Temperature Prediction Analysis Based on Random Forest and ARIMA Model

Zihan Yi

doi:10.6911/WSRJ.202505_11(5).0017

Authors

Zihan Yi

DOI:

https://doi.org/10.6911/WSRJ.202505_11(5).0017

Keywords:

Global temperature change; M - K mutation test; Random forest regression prediction model; ARIMA model; Correlation analysis.

Abstract

This paper focuses on the study of global temperature change trends. By collecting historical temperature data from multiple sources, it conducts descriptive analysis and uses the Mann - Kendall mutation test to explore the temperature change characteristics. Two models, a random forest regression prediction model and an ARIMA - based time series model, are established to predict future global temperatures. The results show that the global temperature is on an upward trend, and the random forest regression prediction model has a higher fitting degree with the actual data. Additionally, the paper analyzes the spatiotemporal evolution of global temperature, the correlation between natural disasters and global temperature changes, and proposes measures to slow down global warming.

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