变量阶段大小演变的参与式学习与内核递归最小方程应用于巴西的天然气价格预测
Eduardo Ravaglia Campos Queiroz1, Kaike Sa Teles Rocha Alves2, Fernando Luiz Cyrino Oliveira1
1Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ Brazil.
Evolving systems
|April 16, 2024
概括
这项研究引入了一种新的机器学习模型,用于准确地预测柴油油价的时间序列. 变量步骤大小演变的参与式学习与内核递归最小方程 (VS-ePL-KRLS) 模型显示了比现有方法更好的准确性.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 计量经济学 计量经济学
背景情况:
- 准确的预测模型对于商业决策至关重要.
- 时间序列预测中的机器学习对于处理信息和发现知识至关重要.
研究的目的:
- 开发和评估一种用于预测每周柴油价格的新型机器学习模型.
- 评估模型的准确性和计算性能,用于每两周和每月的时间.
主要方法:
- 使用核心递归最小方程 (VS-ePL-KRLS) 模型实现可变步骤大小演变的参与式学习模型.
- 在巴西水平上预测S500和S10柴油油价的应用程序.
主要成果:
- 与文献中的现有模型相比,VS-ePL-KRLS模型显示出更高的准确性.
- 该模型在所有分析的时间序列中保持了计算性能.
结论:
- VS-ePL-KRLS模型为商品价格的时间序列预测提供了一个有效的解决方案.
- 该模型在不影响计算效率的情况下提供了更高的准确性.
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