Smart Forecasting of Carbon Prices Using Machine Learning and Neural Networks: When ARIMA Meets XGBoost and LSTM
Giorgos Kotsompolis, Panagiotis T. Cheilas, Konstantinos Ν. Konstantakis, Evangelos Sfakianakis, Stéphane Goutte, Panayotis G. Michaelides
Abstract
ABSTRACT Accurate prediction of carbon prices is crucial for policymakers, investors, and other participants in emissions trading schemes (ETS) and during regulatory transitions. In this work, carbon price movements are forecasted using a nonlinear ARIMA model as the baseline, alongside XGBoost and LSTM as competing models. The widely adopted XGBoost model is a machine learning (ML) technique, while the LSTM model belongs to the class of Recurrent Neural Network (RNN) models. To harness the predictive strengths of both approaches, we also employ a hybrid model that averages forecasts from the LSTM and XGBoost models. The dataset used in this study is in daily format, ranging from December 1, 2010, to January 10, 2025. The results show that both XGBoost and LSTM outperform the baseline ARIMA model. Furthermore, the hybrid model demonstrates statistically significant improvements in forecasting accuracy compared to the baseline model. These findings suggest that ML‐ and RNN‐based approaches can serve as effective alternatives to traditional statistical and econometric models in carbon pricing forecasting.