Work

Carbon Trading Price Prediction Based on Complex Wavelet Transform and Gaussian Process Twice Decompose

Year: 2025

Type: conference-paper

Abstract: With the advancement of China's carbon trading market, accurate price prediction is essential for market stability. This study proposes a dual decomposition strategy using Complex Wavelet Transform and Gaussian Process Regression, with Kolmogorov complexity and log marginal likelihood for layer determination, and Approximate Entropy for sesquence reconstruction. A Gated Recurrent Unit serves as the prediction model, optimized via the CPO Algorithm, achieves high accuracy in predicting carbon prices in Wuhan and Beijing.

Source: International Conference on Communication Networks and Machine Learning (CNML)

DOI: 10.1145/3728199.3728246

Authors: Changyue Wang, Cong Zhang, Xinyu Liu, Nan Wang, Jingyu Guo, Xiaofeng Tang

Institutions: China Agricultural University, Shenyang Jianzhu University, Northeast Agricultural University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Climate action

Open Access status: hybrid

OpenAlex ID: W4410812238

FWCI: 7.152

Cites: 10

Cited by: 3

Related to: 10