An optimized and interpretable carbon price prediction: Explainable deep learning model

Gehad Ismail Sayed, Eman I. Abd El-Latif, Ashraf Darwish, Vaclav Snasel and Aboul Ella Hassanien

Chaos, Solitons & Fractals, 2024, vol. 188, issue C

Abstract: Accurate prediction of carbon prices is crucial for a stable market, enabling informed decision-making and strategic planning. Over the years, several models for predicting carbon prices have been proposed to enhance accuracy. However, previous research has primarily focused on improving accuracy, often neglecting the importance of making the findings understandable and meaningful. This paper aims to bridge that gap by not only improving prediction accuracy but also ensuring that the results are transparent and comprehensible, thus contributing to more effective and informed decision-making in the carbon market. An optimized Long Short-Term Memory (LSTM) network enhanced with the modified light spectrum optimizer (MLSO) is proposed to improve carbon price prediction accuracy. Additionally, the paper incorporates Explainable AI (XAI) techniques to interpret the results, bridging the gap between accuracy and interpretability. The proposed model is evaluated on carbon price historical transaction data acquired from Investing.com and tested on eight other benchmark datasets with different characteristics. The proposed model achieved 0.66 root mean square error (RMSE), 0.99 R2, 0.37 mean absolute error (MAE), 0.15 mean absolute percentage error (MAPE), and 0.44 mean square error (MSE). The results showed that low price, high price, and open price features have the highest significance in driving the model's predictions in comparison to other features like date, volume, and price change features. Additionally, the results indicate that the year, day, and month do not significantly influence the carbon price. The proposed model outperforms state-of-the-art models and other well-known machine learning algorithms according to the experimental results. Moreover, the results indicate that the predictive capability of the proposed model serves as a valuable tool for investors and carbon traders to understand the factors influencing price changes, optimize their strategies, and minimize risk.

Keywords: Carbon price; Carbon emission; Light spectrum optimizer; Explainable deep-learning; Long short term memory; Hyper-parameters optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:188:y:2024:i:c:s0960077924010853

DOI: 10.1016/j.chaos.2024.115533

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