Predicting Climate Change: A Linear Regression & Convex Optimization Approach

Climate prediction is a complex and challenging task. Machine learning techniques, particularly Linear Regression combined with Convex Optimization, provide a promising alternative to traditional statistical models by improving predictive accuracy and reducing error. This research explores the application of these techniques to enhance the reliability of climate predictions. To ensure robustness and improve generalizability, the model was tested across multiple datasets, including Vostok Ice Core and ERA5, which capture historical and modern climate data. The results demonstrate a significant reduction in prediction error, highlighting the effectiveness of Convex Optimization in improving prediction accuracy and the model’s capacity to generalize across different climate datasets.