在关联域中估计静止和非静止移动平均过程
Martin Dodek1, Eva Miklovičová1
1Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava, Bratislava, Slovakia.
PloS one
|January 27, 2025
概括
本研究提出了一种用于估计移动平均线过程的新方法,无论是静止的还是非静止的. 它使用非线性方程和代算法有效地解决自相关性匹配问题,以改进参数估计.
科学领域:
- 信号处理 信号处理
- 时间序列分析时间序列分析
- 统计建模 统计建模
背景情况:
- 移动平均 (MA) 过程是时间序列分析的基础.
- 准确的参数估计对于建模静止和非静止数据至关重要.
- 现有的MA参数估计方法可能是计算密集的或范围有限的.
研究的目的:
- 引入一种新的离线方法来估计静止移动平均过程.
- 扩展非静态移动平均线过程的高效在线估计方法.
- 用一种独特的技术来解决自相关函数匹配问题.
主要方法:
- 利用彩色噪声和MA系数的自相关函数之间的平等.
- 使用牛顿-拉普森和莱文伯格-马奎特算法推导和解决一个非线性方程系统.
- 为在线非静止参数更新开发具有指数式遗忘的递归公式.
主要成果:
- 证明多个对称的解决方案和可行性条件.
- 对非静止病例的估计复杂性显著降低,导致可解决的三角形系统.
- 在线估计非静止过程中的有效参数调整.
结论:
- 拟议的方法为静态和非静态MA过程估计提供了有效和高效的方法.
- 该技术为自相关性匹配问题提供了强有力的解决方案.
- 数字实验验证方法的性能与现有技术相比.
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