What question did this study set out to answer?

This research aims to enhance carbon price forecasting by developing a novel interval prediction model that integrates statistical frameworks and neural networks.

May 10, 2026Open Access

A novel interval prediction model based on LUBE and decomposition ensemble for carbon price forecasting and trading

Puntos clave

This research aims to enhance carbon price forecasting by developing a novel interval prediction model that integrates statistical frameworks and neural networks.
Proposed a novel interval prediction model combining QD-LUBE and decomposition-ensemble methodologies.
Utilized Variational Mode Decomposition (VMD) for separating price series into trend and residual components.
Employed Multilayer Perceptron (MLP) for predicting trends and Bidirectional Gated Recurrent Unit (Bi-GRU) for capturing residual dynamics.
The model provides refined interval forecasts compared to traditional point forecasting methods.
Demonstrated economic value through a simple trend-based trading strategy that incorporates uncertainty quantification.

Resumen

Abstract Existing research on carbon price forecasting has predominantly focused on deterministic point forecasts. However, interval forecasting offers richer informational content for decision-makers and facilitates more effective risk management in practical applications. To address this gap, this paper proposes a novel interval prediction model that integrates a Quality-Driven Lower Upper Bound Estimation (QD-LUBE) framework with a decomposition-ensemble methodology. The proposed approach overcomes the inherent limitations of conventional LUBE by adopting a differentiable loss function derived from rigorous statistical principles, thereby enabling efficient neural network optimization via gradient descent. Subsequently, Variational Mode Decomposition (VMD) is employed to separate the original price series into a low-frequency trend component and a high-frequency residual component. Within the QD-LUBE framework, a Multilayer Perceptron (MLP) is utilized to predict the trend, while a Bidirectional Gated Recurrent Unit (Bi-GRU) is designed capture the dynamics of the residuals. The integration of these two outputs yield refined interval forecasts. Finally, the effectiveness of the proposed model is validated utility is further demonstrated through a simple trend-based trading strategy, underscoring the economic value of incorporating uncertainty quantification into carbon trading decisions.

Me gusta

Guardar

Ver artículo completo