Carbon price forecasting with complex network and extreme learning machine

Hua Xu, Minggang Wang, Shumin Jiang and Weiguo Yang

Physica A: Statistical Mechanics and its Applications, 2020, vol. 545, issue C

Abstract: Carbon emission price mechanism is the core issue in carbon emission trading. The carbon emission price fluctuation trend is related to the play of the effectiveness of carbon emission trading market, and directly affects the green and low-carbon behavior of enterprises and residents. Therefore, the prediction of carbon price is of great practical significance. This study presents a new carbon price prediction model by using time series complex network analysis technology and extreme learning machine algorithm (ELM). In our model, we first map the carbon price data into a carbon price network (CPN), and then extract the effective information of carbon price fluctuations by using the network topology, and use the extracted effective information to reconstruct the carbon price sample data. With the reconstructed data and the extreme learning machine algorithm, the carbon price network extreme learning machine model (CPN-ELM) is built. To test the validity of the model, we selected the carbon emission price data of the second, third and transition stages of the European Union Emissions Trading System (EU ETS) for empirical analysis, the results show that CPN-ELM can improve the predictive accuracy of ELM in both level accuracy and directional accuracy. Meanwhile, CPN-ELM prediction model has better robustness when facing the random samples, sample data with different frequencies or sample data with structural changes.

Keywords: Carbon price prediction; Complex network; Extreme learning machine; Green and low-carbon behavior (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119316097
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119316097

DOI: 10.1016/j.physa.2019.122830

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().