An optimal hybrid framework for carbon price prediction using time series analysis

Abstract

Proper carbon price forecasting is the key to the dynamics of the emissions trading system (ETS) and assists market participants. The objectives of this study are to examine the price prediction of the carbon price, taking as the input previous price histories of major price ETS markets, but it is not a classification problem, but a time-series regression task. The aim of the forecasting is to forecast the value of future carbon prices at short-term scales with the help of supervised learning models. The structure of a hybrid deep learning network is suggested, whereby a Long Short-Term Memory (LSTM) network is trained with an Enhanced Pelican Optimization (EPO) algorithm to enhance hyperparameter selection. Model performance is measured in a rigorously chronologically rolling-origin validation scheme to prevent data leakage and may be more realistic for forecasting in the real world. Regular regression-based performance measures, such as MAE, RMSE, and MAPE, are used to guarantee that performance is comparable to the literature on carbon price forecasting. The experimental findings indicate that the proposed methodology yields consistent falls in error in comparison with the general statistical and deep learning baselines on the datasets analyzed. Although the results show that the methodology has been improved in terms of R2 in forecasts, the findings are limited to the chosen markets and forecast horizons. This makes the findings map conclusions to the predictions of performance and strength, and not a direct economic or policy influence. Further employment will involve more analysis of other markets, horizons, and economic utility analysis.