Carbon price forecasting models based on big data analytics

After the establishment of the European Union's Emissions Trading System (EU-ETS) carbon pricing attracted many researchers. This paper aims to develop a prediction model that anticipates future carbon prices given a real-world data set. We treat the carbon pricing issue as part of big data analytics to achieve this goal. We apply three fundamental methodologies to characterize the carbon price. First method is the artificial neural network, which mimics the principle of human brain to process relevant data. As a second approach, we apply the decision tree algorithm. This algorithm is structured through making multiple binary decisions, and it is mostly used for classification. We employ two different decision tree algorithms, namely traditional and conditional, to determine the type of decision tree that gives better results in terms of prediction. Finally, we exploit the random forest, which is a more complex algorithm compared to the decision tree. Similar to the decision tree, we test both traditional and conditional random forest algorithms to analyze their performances. We use Brent crude futures, coal, electricity and natural gas prices, and DAX and S&P Clean Energy Index as explanatory variables. We analyze the variables' effects on carbon price forecasting. According to our results, S&P Clean Energy Index is the most influential variable in explaining the changes in carbon price, followed by DAX Index and coal price. Moreover, we conclude that the traditional random forest is the best algorithm based on all indicators. We provide the details of these methods and their comparisons. Abbreviations ANNArtificial Neural Network CDTConditional Decision Tree CRFConditional Random Forest DAXStock market index consisting of 30 major German companies DTDecision Tree ETSEmissions Trading System EU-ETSEuropean Union Emissions Trading System FFNNFeedforward Neural Network GHGGreenhouse Gases IncNodePurityIncrease in Node Purity MAEMean Average Error MAPEMean Average Percentage Error MSEMean Square Error RBFNRadial Basis Function Network RFRandom Forest RMSERoot Mean Square Error RSR-Squared (Coefficient of determination) TDTTraditional Decision Tree TRFTraditional Random Forest VARVector Auto Regression VMDVariational Mode Decomposition % IncMSEPercentage increase in mean square error