Carbon price prediction based on a scaled PCA approach
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pone.0296105
Download full text from publisher
References listed on IDEAS
- Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
- Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
- Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022.
"Scaled PCA: A New Approach to Dimension Reduction,"
Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
- Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," CEMA Working Papers 678, China Economics and Management Academy, Central University of Finance and Economics.
- Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
- Sun, Wei & Zhang, Junjian, 2022. "A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction," Energy, Elsevier, vol. 253(C).
- Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Mehmet Sarıkoç & Mete Celik, 2025. "PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2249-2315, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Na Fu & Liyan Geng & Junhai Ma & Xue Ding, 2023. "Price, Complexity, and Mathematical Model," Mathematics, MDPI, vol. 11(13), pages 1-30, June.
- Beibei Hu & Yunhe Cheng, 2023. "Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction," Energies, MDPI, vol. 16(11), pages 1-22, May.
- Qin, Chaoyong & Qin, Dongling & Jiang, Qiuxian & Zhu, Bangzhu, 2024. "Forecasting carbon price with attention mechanism and bidirectional long short-term memory network," Energy, Elsevier, vol. 299(C).
- Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
- Beibei Hu & Yunhe Cheng, 2023. "Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-24, December.
- Yingjie Zhu & Yongfa Chen & Qiuling Hua & Jie Wang & Yinghui Guo & Zhijuan Li & Jiageng Ma & Qi Wei, 2024. "A Hybrid Model for Carbon Price Forecasting Based on Improved Feature Extraction and Non-Linear Integration," Mathematics, MDPI, vol. 12(10), pages 1-26, May.
- Jiang, Meiqin & Che, Jinxing & Li, Shuying & Hu, Kun & Xu, Yifan, 2025. "Incorporating key features from structured and unstructured data for enhanced carbon trading price forecasting with interpretability analysis," Applied Energy, Elsevier, vol. 382(C).
- Wu, Han & Du, Pei, 2024. "Dual-stream transformer-attention fusion network for short-term carbon price prediction," Energy, Elsevier, vol. 311(C).
- Zhang, Ditian & Tang, Pan, 2023. "Forecasting European Union allowances futures: The role of technical indicators," Energy, Elsevier, vol. 270(C).
- Wang, Yue & Wang, Zhong & Luo, Yuyan, 2024. "A hybrid carbon price forecasting model combining time series clustering and data augmentation," Energy, Elsevier, vol. 308(C).
- Song, Chao & Wang, Tao & Chen, Xiaohong & Shao, Quanxi & Zhang, Xianqi, 2023. "Ensemble framework for daily carbon dioxide emissions forecasting based on the signal decomposition–reconstruction model," Applied Energy, Elsevier, vol. 345(C).
- Ding, Lili & Zhang, Rui & Zhao, Xin, 2024. "Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks," Energy, Elsevier, vol. 288(C).
- Ma, Yong & Zhou, Mingtao & Li, Shuaibing, 2024. "Weathering market swings: Does climate risk matter for agricultural commodity price predictability?," Journal of Commodity Markets, Elsevier, vol. 36(C).
- Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
- Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
- Xian, Sidong & Feng, Miaomiao & Cheng, Yue, 2023. "Incremental nonlinear trend fuzzy granulation for carbon trading time series forecast," Applied Energy, Elsevier, vol. 352(C).
- Yin, Hao & Yin, Yiding & Li, Hanhong & Zhu, Jianbin & Xian, Zikang & Tang, Yanshu & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhang, Haitao & Xie, Zhifeng & Meng, Anbo, 2025. "Carbon emissions trading price forecasting based on temporal-spatial multidimensional collaborative attention network and segment imbalance regression," Applied Energy, Elsevier, vol. 377(PA).
- Liu, Shan & Li, Ziwei, 2023. "Macroeconomic attention and oil futures volatility prediction," Finance Research Letters, Elsevier, vol. 57(C).
- Wen, Danyan & Zhang, Zihao & Nie, Jing & Cao, Yang, 2024. "Investor attention and anomalies: Evidence from the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 96(PB).
- Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0296105. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.