School of Business, Anhui University, Hefei, 230601, Anhui, China.
Center for Applied Mathematics, Anhui University, Hefei, 230601, China.
Environ Sci Pollut Res Int. 2023 Sep;30(42):95840-95859. doi: 10.1007/s11356-023-29028-0. Epub 2023 Aug 10.
Accurate carbon price prediction is a crucial task for the carbon trading market. Previous studies have ignored the impact of online data and are limited to point predictions, which brings challenges to the accurate forecasting of carbon prices. To address those issues, this paper proposes an interval-valued carbon price forecasting method based on web search data and social media sentiment. First, we collect web search data and social media sentiment to improve prediction performance by synthesizing multiple types of data information. Second, we employ principal component analysis (PCA) to preprocess high-dimensional web search data, and utilize BosonNLP for quantifying social media information, thereby enhancing the predictability of the dataset. Subsequently, a variational mode decomposition (VMD) is applied to the carbon price and online data, followed by utilizing particle swarm optimization support vector regression (PSO-SVR) to predict each sub-modes and summing them up to obtain the ultimate forecasting outcome. Finally, using carbon prices in Guangdong and Hubei provinces as case studies, the experimental results demonstrate that web search data and social media sentiment significantly enhance the predictive accuracy of interval-valued carbon prices. Furthermore, the proposed VMD-PSO-SVR outperforms other comparative models in the accuracy and reliability of interval-valued forecasting.
准确的碳价预测是碳交易市场的一项关键任务。以往的研究忽略了网络数据的影响，仅限于点预测，这给碳价的准确预测带来了挑战。针对这些问题，本文提出了一种基于网络搜索数据和社交媒体情绪的区间碳价预测方法。首先，我们收集网络搜索数据和社交媒体情绪，通过综合多种类型的数据信息来提高预测性能。其次，我们采用主成分分析（PCA）对高维网络搜索数据进行预处理，并利用 BosonNLP 对社交媒体信息进行量化，从而增强数据集的可预测性。然后，将变分模态分解（VMD）应用于碳价和网络数据，接着利用粒子群优化支持向量回归（PSO-SVR）预测每个子模态，并将它们相加得到最终的预测结果。最后，以广东和湖北两省的碳价为例进行实验，结果表明，网络搜索数据和社交媒体情绪显著提高了区间碳价的预测精度。此外，所提出的 VMD-PSO-SVR 在区间预测的准确性和可靠性方面优于其他比较模型。
