适用于时间变化的VAR模型的平滑在线参数估计,并应用到评估本地现场潜在活动数据的应用
Anass El Yaagoubi Bourakna1, Marco Pinto2, Norbert Fortin3
1King Abdullah University of Science and Technology.
Statistics and its interface
|April 24, 2025
概括
本研究引入了一种新的在线方法,用于估计非静止多变量时间序列数据中的时间变化的光谱性质. 顺在线参数估计 (SOPE) 方法提供实时,计算高效的大脑连接估计,优于高维数据的传统方法.
科学领域:
- 统计 统计 统计 统计
- 神经科学是一个神经科学.
- 信号处理 信号处理
背景情况:
- 多变量时间序列经常表现出具有不断演变的共变量或光谱矩阵的非静止过程.
- 目前用于估计时间变化的光谱性质的方法通常是追溯的,限制实时自适应控制应用.
- 实时估计光谱大脑连接对于理解动态大脑功能至关重要.
研究的目的:
- 开发一种在线估计程序,以实时更新多变量时间序列中的时间变量参数.
- 为解决高维时间变量向量自回归 (tv-VAR) 模型在线估计的计算挑战.
- 提出一种可靠的方法来实时估计光谱大脑连接.
主要方法:
- 开发了一种流的在线参数估计 (SOPE) 方法,用于实时的参数更新.
- SOPE控制了合理的计算复杂度估计的平滑性,使实时适合高维时间序列.
- 在平均平方误差和计算成本方面,与卡尔曼波器比较SOPE.
主要成果:
- 对于小尺寸的卡尔曼波器,SOPE证明了与卡尔曼波器相似的平均平方误差.
- 索普比卡尔曼波器提供了较低的计算成本,使其可扩展到更高的维度.
- 在臭味记忆任务中,SOPE方法成功地捕获了老鼠海马局域潜在数据中的动态连接变化.
结论:
- 拟议的SOPE方法提供了一种高效且可扩展的解决方案,用于实时估计高维非静止时间序列中的时间变化的光谱性质.
- 对于需要实时分析的应用,例如自适应控制和神经科学研究,SOPE特别有价值.
- SOPE有效地跟踪动态大脑连接,提供了解认知任务期间神经过程的见解.
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