Carbon financial price forecasting based on VMD noise reduction and improved DELM optimized by the WDO algorithm

Jingying Fan; Jiayin Chen; Haoxuan Li

doi:10.54691/bcpbm.v26i.1928

Authors

Jingying Fan
Jiayin Chen
Haoxuan Li

DOI:

https://doi.org/10.54691/bcpbm.v26i.1928

Keywords:

VMD; WDO; DELM; Carbon price; forecasting.

Abstract

Carbon price forecasting can help stabilize the carbon pricing mechanism and reduce carbon market risks. This paper firstly uses the closing price of the Guangzhou carbon exchange to predict carbon prices. Secondly, this paper constructs model based on a wind-driven algorithm (WDO) and deep extreme learning machine (DELM), compared with the results of a backpropagation neural network (BP). The prediction results are reliable, with a 49.81% decrease in mean square error (MSE), which shows that the validity of the hybrid VMD-DELM approach is verified.

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