Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China

Abstract

Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models' universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model's validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants' behavior, which is more practical in the operation of carbon trading markets.

Analysis and forecasting of the dynamics of fuel trade between Turkmenistan and China using the autoregressive integrated moving average (ARIMA) model

“…The ADF coefficient test is a fully parametric way to find a unit root in ARIMA models where the order is unknown [19] . In addition, the ARIMA parameter model is useful for analyzing and predicting electrical signals in plant environments, providing information on stability, and predicting short-term trends [20] . Additionally, a healthcare facility's incidence of acute respiratory infection (ARI) cases has been forecasted using the ARIMA methodology, yielding a forecast of 354 cases for 2019 using the 2.0,1 model [21] .…”

Fluctuations and Forecasting of Carbon Price Based on A Hybrid Ensemble Learning GARCH-LSTM-Based Approach: A Case of Five Carbon Trading Markets in China

“…Yun et al (2020) built a new carbon price prediction method based on an NAGARCHSK-LSTM model, considering the special characteristics of carbon price asymmetry, extreme shock sensitivity and time-varying fluctuations [18]. Zhao et al (2023) used the Adam algorithm to optimize the long-and short-term memory method to predict CTP points. In addition, scholars generally regard the LSTM neural network model (Hochreiter and Schmidhuber, 1997) as the current mainstream machine prediction model [19,20].…”

“…Zhao et al (2023) used the Adam algorithm to optimize the long-and short-term memory method to predict CTP points. In addition, scholars generally regard the LSTM neural network model (Hochreiter and Schmidhuber, 1997) as the current mainstream machine prediction model [19,20]. With the characteristics of time series selection memory and interaction, it can effectively solve the problem of unstable carbon price (Dey et al, 2021;Marzouk et al, 2021;Chen et al, 2021, Yang et al, 2022 [21][22][23][24].…”