基于网络搜索数据和社交媒体情绪的间隔估值碳价格预测方法
Jinpei Liu1,2, Xue Li1, Piao Wang3
1School of Business, Anhui University, Hefei, 230601, Anhui, China.
Environmental science and pollution research international
|August 10, 2023
概括
本研究介绍了一种使用网络搜索数据和社交媒体情绪的间隔估值碳价格预测方法. 该方法显著提高了碳交易市场的预测准确性.
科学领域:
- 环境经济学环境经济学
- 计算金融是指计算金融.
- 数据科学数据科学数据科学
背景情况:
- 准确的碳价格预测对于碳交易市场至关重要.
- 现有的方法往往忽略在线数据,依赖于点预测,限制预测准确度.
- 由于数据有限,准确预测碳价格仍然存在挑战.
研究的目的:
- 提出一个区间估值的碳价格预测方法,结合网络搜索数据和社交媒体情绪.
- 通过合成各种数据源来提高预测性能.
- 提高碳价格预测的准确性和可靠性.
主要方法:
- 收集和综合网络搜索数据和社交媒体情绪.
- 应用主要组件分析 (PCA) 用于高维网页搜索数据的预处理.
- 使用BosonNLP进行社交媒体情绪量化.
- 在碳价格和在线数据上使用变量模式分解 (VMD).
- 使用粒子优化支持向量回归 (PSO-SVR) 进行子模式预测和聚合.
主要成果:
- 网络搜索数据和社交媒体情绪显著提高了区间估值碳价格的预测准确性.
- 拟议的VMD-PSO-SVR模型在区间估值预测准确性和可靠性方面表现出卓越的表现.
- 广东和湖北省的案例研究证实了综合方法的有效性.
结论:
- 整合在线数据来源,如网络搜索趋势和社交媒体情绪,可以有效地改善碳价格预测.
- VMD-PSO-SVR方法为准确可靠的区间估值碳价格预测提供了一个强大的框架.
- 这项研究为碳交易市场参与者和政策制定者提供了宝贵的见解.
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