对于混乱时间序列的通用关系张量
Vasilii A Gromov1, Yury N Beschastnov1, Korney K Tomashchuk1
1School of Data Analysis and Artificial Intelligence, Higher School Economics University, Moscow, Russia.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了时间序列数据存储和预测的通用关系张量. 结合通用z向量和群优化的算法显示了周期和混乱时间序列分析的有希望的结果.
科学领域:
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
- 计算智能是一种计算智能.
背景情况:
- 时间序列数据分析在各种科学领域至关重要.
- 存储和预测时间序列数据的现有方法具有局限性,特别是对于复杂或混乱的数据集.
研究的目的:
- 介绍一种新的离散结构,即通用关系张量,用于有效的时间序列信息存储.
- 使用这种结构开发和评估用于填充,再生和预测时间序列的算法.
- 评估这些算法的性能,特别是对于混乱的时间序列.
主要方法:
- 开发集成通用z向量与群优化技术的算法.
- 使用初始和再生时间序列特征之间的差异度量来评估数据存储和再生质量.
- 采用诸如最大的利亚普诺夫指数和自动相关函数等指标来进行混乱时间序列分析.
主要成果:
- 概括关系张量和相关算法证明了有效的时间序列存储和再生.
- 对于周期性和基准混乱时间序列,取得了相当好的结果.
- 对于现实世界的混乱时间序列数据,观察到令人满意的性能.
结论:
- 提出的通用关系张量为时间序列数据提供了一个可行的离散结构.
- 开发的算法在存储,再生和预测时间序列,包括混乱的时间序列方面表现出有效性.
- 该方法为科学研究中分析复杂时间序列数据提供了有价值的工具.
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