Predicting Carbon Prices: A New Approach to Forecasting Emissions Markets

by

Author: Denis Avetisyan

A novel forecasting framework combining signal processing and deep learning dramatically improves the accuracy of EU carbon price predictions.

Multiple forecasting models were evaluated, revealing varying degrees of accuracy in predicting carbon price fluctuations.
Multiple forecasting models were evaluated, revealing varying degrees of accuracy in predicting carbon price fluctuations.

This study compares deep learning models, including Temporal Convolutional Networks, for forecasting carbon prices while accounting for structural breaks and utilizing wavelet-based denoising techniques.

Accurate forecasting of carbon prices remains a persistent challenge despite increasing demand for informed energy market decisions. This is addressed in ‘Carbon Price Forecasting with Structural Breaks: A Comparative Study of Deep Learning Models’, which investigates novel hybrid frameworks for improved prediction. The research demonstrates that integrating structural break detection, wavelet denoising, and Temporal Convolutional Networks (TCNs) substantially enhances forecasting accuracy for EU carbon prices, reducing errors by up to 74.42% compared to existing methods. Could this approach unlock more reliable modeling of other nonstationary financial time series characterized by complex policy interventions and market dynamics?

The Price of Prediction: Forecasting Carbon Markets

The European Union Emissions Trading System (EU ETS) is foundational to climate policy, yet its success hinges on accurate carbon price forecasting. Precise predictions are vital for informed policymaking and investment in decarbonization technologies. Traditional time series models, such as ARIMA, often struggle with the system’s inherent complexity, driven by fluctuating energy demands and geopolitical factors. Robust analytical tools are therefore essential to account for non-linear relationships and improve forecasting.

The PELT-WT-LSTM(uni) model demonstrates performance in forecasting carbon prices.
The PELT-WT-LSTM(uni) model demonstrates performance in forecasting carbon prices.

A system that seeks to predict the future must first relinquish its insistence on the past.

Identifying the Turning Points: Detecting Structural Shifts

Accurate modeling requires identifying structural breaks—points where the underlying statistical properties of carbon prices change. Simple trend analysis often fails to capture dynamic behavior, necessitating techniques to detect shifts in volatility, mean, or autocorrelation. Methods like PELT, the ICSS Algorithm, and the Bai-Perron Test provide statistical rigor. PELT utilizes penalized likelihood estimation, the ICSS Algorithm employs an information criterion, and the Bai-Perron Test assesses the significance of changes through regression analysis.

Detected structural breakpoints in the carbon price time series are identified through feature analysis.
Detected structural breakpoints in the carbon price time series are identified through feature analysis.

Detecting these breakpoints enables adaptive modeling, adjusting parameters to reflect new market regimes, enhancing forecasting and risk management by acknowledging the evolving nature of carbon price dynamics.

Beyond GARCH: Advanced Forecasting with Deep Learning

While GARCH models capture volatility clustering, they often struggle with structural breaks and complex dependencies. Deep learning architectures—LSTM networks, GRU, and TCN—offer increased capacity to learn intricate patterns and incorporate external features. These models excel at representing non-linear relationships and long-range dependencies.

The proposed framework combines structural break detection, wavelet denoising, and sequence models—LSTM, GRU, and TCN—to forecast carbon prices in both univariate and multivariate analyses.
The proposed framework combines structural break detection, wavelet denoising, and sequence models—LSTM, GRU, and TCN—to forecast carbon prices in both univariate and multivariate analyses.

Recent research demonstrates that combining wavelet transform for data denoising with deep learning further enhances accuracy. The proposed PELT-WT-TCN model—integrating piecewise linear estimation, wavelet transform, and a TCN—achieved state-of-the-art performance in carbon price forecasting.

Measuring Prediction: Evaluating Forecasting Performance

Rigorous evaluation is essential, utilizing established metrics—Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared—to quantify forecasting accuracy within the EU ETS. The proposed model demonstrated superior performance, achieving a Mean Absolute Error (MAE) of 1.1855, a Root Mean Squared Error (RMSE) of 1.5866, a Mean Absolute Percentage Error (MAPE) of 1.6451%, and a Coefficient of Determination (R²) of 0.9888.

A comparison of model performance metrics reveals the relative strengths of different approaches.
A comparison of model performance metrics reveals the relative strengths of different approaches.

Improved forecasting accuracy informs better decision-making for policymakers, traders, and investors. Abstractions age, principles don’t.

The pursuit of accurate carbon price forecasting, as demonstrated in this study, necessitates a ruthless paring away of noise to reveal underlying structure. Models are often burdened with complexity, attempting to account for every fluctuation; however, this research highlights the power of simplification through wavelet decomposition and the targeted application of Temporal Convolutional Networks. As Bertrand Russell observed, “The point of philosophy is to state things in such a way that they are trivially true.” This principle echoes in the methodology – the aim isn’t to predict every nuance, but to establish a fundamentally sound and demonstrably accurate baseline, achieved through clarity of approach and a focused application of deep learning techniques to time series analysis. The minimization of variables directly improves predictive power.

What’s Next?

The apparent success of combining structural break detection, wavelet decomposition, and Temporal Convolutional Networks does not, of course, signify an arrival. It merely clarifies the persistent inadequacy of simpler approaches. If forecasting accuracy is the sole metric, this work offers incremental improvement. However, the fundamental problem remains: carbon pricing, like all markets driven by policy and speculation, is not a purely temporal phenomenon. To treat it as such is to mistake a symptom for the disease.

Future iterations should not chase diminishing returns in model complexity. Instead, the field must confront the untidiness of incorporating exogenous variables – geopolitical events, regulatory shifts, even the whims of influential actors. These factors resist neat quantification, yet dominate the carbon market’s behavior. A truly robust model will not predict despite these uncertainties, but because of them, embracing a probabilistic framework that acknowledges inherent unpredictability.

The current focus on algorithmic refinement risks obscuring a more fundamental point. If the goal is not simply to predict price movements, but to inform effective climate policy, then accuracy is a secondary concern. A deliberately conservative forecast, even if less precise, may be preferable if it encourages bolder action. The pursuit of perfect prediction, in this context, appears less like scientific inquiry and more like a sophisticated form of procrastination.

Original article: https://arxiv.org/pdf/2511.04988.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-11-10 13:04