Carbon Price Prediction by Incorporating Fossil Fuel Prices Using Long Short-Term Memory with Temporal Pattern Attention (TPA-LSTM)
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Keywords:
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Greenhouse Gas, Carbon Price, Carbon Market, Long Short-Term Memory, Temporal Pattern Attention
Abstract
Reliable carbon price prediction can help to stabilize the carbon market, minimize financial risks for investors, and encourage innovation in green industries. The forecasts have a crucial role in reaching advanced goals for reducing emissions, aiding the shift toward an economy with reduced carbon emissions, and lessening the adverse effects of climate change overall. This paper proposes applications of LSTM with Temporal Pattern Attention (TPA-LSTM) to predict carbon price fluctuations. The prediction of carbon price fluctuations not only draws on its own historical information but also from its main predictors, including fossil fuel prices from 2018 to 2023. The TPA-LSTM method is a combined method that uses the LSTM layer as the initial input of the model. Furthermore, the output generated by the LSTM layer serves as the input to the TPA layer, which is then used to predict the carbon price for the following day. The model is tested by predicting the test data and calculating the evaluation results. The experimental results indicate that TPA-LSTM has surpassed other models in accuracy by showing better performance based on MSE, RMSE, MAE, and MAPE metrics.
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Zhao, X., M. Han, L. Ding, and W. Kang (2018). Usefulness of Economic and Energy Data at Different Frequencies for Carbon Price Forecasting in the EU ETS. Applied Energy, 216; 132–141.
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Dama, F. and C. Sinoquet (2021). Time Series Analysis and Modeling to Forecast: A Survey.
Duan, K., X. Ren, Y. Shi, T. Mishra, and C. Yan (2021). The Marginal Impacts of Energy Prices on Carbon Price Variations: Evidence from a Quantile-on-Quantile Approach. Energy Economics, 95; 105131.
Duan, K., R. Wang, S. Chen, and L. Ge (2023). Exploring the Predictability of Attention Mechanism with LSTM: Evidence from EU Carbon Futures Prices. Research in International Business and Finance, 59; 101314.
Gong, H., Y. Li, J. Zhang, B. Zhang, and X. Wang (2024). A New Filter Feature Selection Algorithm for Classification Task by Ensembling Pearson Correlation Coefficient and Mutual Information. Engineering Applications of Artificial Intelligence, 131; 107865.
Green, J. F. (2021). Does Carbon Pricing Reduce Emissions? A Review of Ex-Post Analyses. Environmental Research Letters, 16(4); 043004.
Han, M., L. Ding, X. Zhao, and W. Kang (2019). Forecasting Carbon Prices in the Shenzhen Market, China: The Role of Mixed-Frequency Factors. Energy, 171; 69–76.
He, K., Q. Yang, L. Ji, J. Pan, and Y. Zou (2023). Financial Time Series Forecasting with the Deep Learning Ensemble Model. Mathematics, 11(4); 1054.
Huang, Y., X. Dai, Q. Wang, and D. Zhou (2021). A Hybrid Model for Carbon Price Forecasting Using GARCH and Long Short-Term Memory Network. Applied Energy, 285; 116485.
Li, J. and X. Wang (2019). Examination of the Transmission Mechanism of Energy Prices and Carbon Prices Based on the VAR Model. Energy Reports, 5; 1398–1406.
Narassimhan, E., K. S. Gallagher, S. Koester, and J. R. Alejo (2018). Carbon Pricing in Practice: A Review of Existing Emissions Trading Systems. Climate Policy, 18(8); 967–991.
Nourry, M. and B. Seetanah (2020). Testing the Impact of Renewable Energy and Oil Price on Carbon Emission Intensity in the Transportation Sector. Renewable Energy, 151; 1212–1220.
Pradana, Y. A., I. Mukhlash, M. I. Irawan, and E. R. Putri (2023). Carbon Price Prediction in the European Market Using Deep Learning. In 2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pages 307–314.
Ren, C. and A. Lo (2017). Emission Trading and Carbon Market Performance in Shenzhen, China. Applied Energy, 193; 414–425.
Sayed, G. I., E. I. Abd El-Latif, A. Darwish, V. Snasel, and A. E. Hassanien (2024). An Optimized and Interpretable Carbon Price Prediction: Explainable Deep Learning Model. Chaos, Solitons & Fractals, 188; 115533.
Shen, D., Z. Tong, and J. W. Goodell (2024). Do Online Message Boards Convey Cryptocurrency-Specific Information? International Review of Financial Analysis, 91; 102950.
Shih, S.-Y., F.-K. Sun, and H.-Y. Lee (2019). Temporal Pattern Attention for Multivariate Time Series Forecasting. Machine Learning, 108; 1421–1441.
Stavins, R. N. (2020). The Future of US Carbon-Pricing Policy. Environmental and Energy Policy and the Economy, 1(1); 8–64.
Tawakuli, A., B. Havers, V. Gulisano, D. Kaiser, and T. Engel (2024). Survey: Time-series data preprocessing: A survey and an empirical analysis. Journal of Engineering Research.
Ullah, A., M. Dogan, A. Pervaiz, A. A. A. Bukhari, H. T. Akkus, and H. Dogan (2024). The Impact of Digitalization, Technological and Financial Innovation on Environmental Quality in OECD Countries: Investigation of N-Shaped EKC Hypothesis. Technology in Society, 77; 102484.
Wang, N. (2017). Forecasting of Carbon Price Based on Boosting-ARMA Model. Statistical Information Forum, 32; 28–34.
Wei, L., D. Guo, Z. Chen, J. Yang, and T. Feng (2023). Forecasting Short-Term Passenger Flow of Subway Stations Based on the Temporal Pattern Attention Mechanism and the Long Short-Term Memory Network. ISPRS International Journal of Geo-Information, 12; 25.
Yang, X., J. Liang, J. Jiang, and M. Jun-Long (2018). Price Forecasting Algorithm of Carbon Emission Rights Under Multiscale Fractal Characteristics. Control Theory & Applications, 35; 224–231.
Yani, I., Y. Resti, F. Burlian, and A. Yani (2021). Prediction of Plastic-Type for Sorting System Using Fisher Discriminant Analysis. Science and Technology Indonesia, 6(4); 313–318.
Zhang, C. and X. Yang (2016). Price Prediction of China’s Regional Carbon Market Based on Multi-Frequency Combination Model. System Engineering Theory & Practice, 36; 3017–3025.
Zhao, X., M. Han, L. Ding, and W. Kang (2018). Usefulness of Economic and Energy Data at Different Frequencies for Carbon Price Forecasting in the EU ETS. Applied Energy, 216; 132–141.
Zhu, B., D. Han, P. Wang, Z. Wu, T. Zhang, and Y. M. Wei (2017). Forecasting Carbon Price Using Empirical Mode Decomposition and Evolutionary Least Squares Support Vector Regression. Applied Energy, 191; 521–530.
Authors
Mujiono, E. P. U. P., Mukhlash, I., Pradana, Y. A., Putri, E. R., & Irawan, . M. I. (2025). Carbon Price Prediction by Incorporating Fossil Fuel Prices Using Long Short-Term Memory with Temporal Pattern Attention (TPA-LSTM). Science and Technology Indonesia, 10(3), 856–865. https://doi.org/10.26554/sti.2025.10.3.856-865
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