Multi-step-ahead and interval carbon price forecasting using transformer-based hybrid model

Yue Wang · Wang Zhong · Wang Xiaoyi · Xinyu Kang

28 citations0/0 files storedEnvironmental Science and Pollution Research

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Stock Market Forecasting Methods Forecasting Techniques and Applications Energy Load and Power Forecasting

Abstract

Abstract Accurate and stable carbon price forecasts provide a reference for the stability of the carbon market and significantly improve investment and operational decisions. However, due to the non-linear and non-stationarity characteristics of carbon price series and its complex fluctuation features, realizing this goal is still a significant challenge, and researchers usually ignore multi-step and interval forecasting. To accurately predict the carbon price, a novel hybrid multi-step and interval carbon price forecasting model is proposed in this study, based on Hampel identifier(HI), time-varying filtering-based empirical mode decomposition (TVFEMD), and Transformer. Firstly, HI identifies and corrects outliers in carbon price. Then, carbon price is decomposed by TVFEMD into several intrinsic mode functions(imfs) to reduce the non-linear and non-stationarity of carbon price, to obtain more regular features in series, and these imfs are reconstructed by sample entropy(SE). Subsequently, the Orthogonal Array Tuning Method(OATM) is used to optimize the Transformer's hyperparameters to obtain the optimal model structure. Finally, The Transformer after hyperparameter optimization and quantile loss function is used to perform multi-step and interval forecasting on each part of the reconstruction, and the final prediction result is obtained by summing them up. Five pilot carbon trading markets in China were selected as experimental objects to verify the proposed model's prediction performance, and a variety of benchmark models and evaluation indicators were selected for comparison and analysis. The experimental results show that the proposed hybrid model is superior to the benchmark model in most aspects, and the interval forecast can well describe the uncertainty of carbon price fluctuations. Therefore, the proposed hybrid model is a reliable carbon price forecasting tool that can provide a reliable reference for policymakers and investors.

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2008Modeling the price dynamics of CO2 emission allowances 2022Carbon price forecasting based on CEEMDAN and LSTM 2012Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology 2013Forecasting carbon futures volatility using GARCH models with energy volatilities 2021A hybrid model for carbon price forecastingusing GARCH and long short-term memory network 2017Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression 2017A Multi-Horizon Quantile Recurrent Forecaster 2019A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network

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