Integrating Machine Learning and Time Series Forecasting for Climate Change Analysis

Climate change has emerged as a significant global challenge, necessitating accurate prediction and analysis of weather patterns. This study explores the application of machine learning (ML) algorithms, Random Forest (RF), and Extreme Gradient Boosting (XGBoost), in conjunction with the ARIMA (AutoRegressive Integrated Moving Average) model for climate change forecasting. Historical weather data, including temperature, humidity, and precipitation, were used to train and evaluate the models. ARIMA was utilized for time series forecasting, while RF and XGBoost were employed for pattern recognition and anomaly detection. The hybrid approach enhanced prediction accuracy and provided deeper insights into long-term climate trends. Comparative analysis showed that the integrated models outperformed standalone techniques in terms of precision and robustness. This research highlights the potential of combining statistical and ML models for efficient and reliable climate prediction, offering a valuable tool for environmental monitoring and policy-making.