Carbon futures price forecasting based on feature selection

Forecasting carbon futures prices is a challenging task due to the complex and dynamic factors influencing them. Accurate forecasting can aid carbon market participants in hedging and optimizing their trading strategies. In this paper, we propose a novel feature selection method based on importance measures, aimed at selecting the most relevant and informative features for forecasting carbon futures prices. Our method introduces Gaussian noise to the input features, calculates the importance scores of the features, and determines the optimal threshold value for feature selection. We train and test different forecasting models on both the original and noisy feature sets using a 5-fold cross-validation approach. The importance score of each feature is calculated based on the error difference between the original and noisy feature sets. The optimal threshold value is determined based on the minimum prediction error obtained by ranking the features. We combine our feature selection method with different models to forecast carbon futures prices. The experimental results demonstrate that our method can effectively select useful features, outperforming variance thresholding and analysis of variance in feature selection. Moreover, our feature selection approach improves the prediction accuracy of different models. Our method is also robust in enhancing prediction accuracy across different models, test sets, time periods, and Gaussian noise levels.