Time-series analysis with small and faulty data: L1-norm decompositions of Hankel matrices
Time-series analysis with small and faulty data: L1-norm decompositions of Hankel matrices
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小数据和错误数据的时间序列分析：Hankel 矩阵的 L1 范数分解
DOI：
10.1117/12.2619243
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
G. Sklivanitis
中科院分区：
文献类型：
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作者：
Georgios I. Orfanidis;D. Pados;G. Sklivanitis
在快速发展的自主系统领域，非平稳环境中的实时操作和监测经常依赖于对可能部分不可靠、缺失或故障的短序列感测数据的分析（过滤/预测）。汉克尔矩阵表示和分解是一种无模型的方法，利用近年来线性代数方法的进步，在时间序列数据分析中越来越流行。在这项工作中，我们建立了Hankel矩阵的新颖l1范数分解对部分故障感知序列提供了强大的抵抗力，因此，为自治系统的鲁棒实时监测创建了一个强大的新框架。本文的发现得到了大量人工数据实验的说明和支持。
In the rapidly advancing field of autonomous systems, real-time operation and monitoring in non-stationary environments frequently relies on analysis (filtering/prediction) of short sequences of sensed data that may be partly unreliable, missing, or faulty. Hankel-matrix representation and decomposition is a model-free approach that is becoming increasingly popular for the analysis of time-series data taking advantage of the progress in linear algebra methods in past years. In this work, we establish that novel L1-norm decompositions of Hankel matrices offer sturdy resistance against partially faulty sensed sequences and, therefore, creates a strong new framework for robust real-time monitoring of autonomous systems. The findings in this paper are illustrated and supported by extensive experimentation on artificial data.