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arXiv 2211.03337math.AT
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时间序列数据的精确多参数持续同调:快速且可变的拓扑推断

Exact multi-parameter persistent homology of time-series data: Fast and variable topological inferences

Institute of Mathematics for Industry, Kyushu University(九州大学产业数学研究所);POSTECH Mathematical Institute for Data Science (MINDS)(POSTECH数据科学数学研究所(MINDS))

Keunsu Kim, Jae-Hun Jung

AI总结:

本文提出了一种基于李乌利尔托罗斯的精确多参数持续同调方法,用于高效分析时间序列数据,显著降低计算复杂性并提升性能。

摘要:

我们提出了一种新的精确多参数持续同调方法,用于分析时间序列数据,利用李乌利尔托罗斯。在拓扑数据分析(TDA)领域,传统分析时间序列数据的方法通常涉及滑动窗口嵌入。从塔肯斯嵌入定理的角度出发,我们证明了在TDA中分析李乌利尔托罗斯的合理性,并讨论了李乌利尔托罗斯与滑动窗口嵌入方法的异同。我们开发了一种基于傅里叶分解的多参数过滤方法,并提供了持续同调的精确公式及其多参数过滤的一参数简化。传统的通过滑动窗口分析时间序列数据的TDA方法已知计算成本很高,但所提出的方法能够迅速获得具有李乌利尔托罗斯对称性的持续条形码公式,这显著降低了计算复杂性,同时在性能上与现有TDA方法相比相当或更优。此外,所提出的方法通过在多参数过滤空间中探索不同的过滤射线,提供了一种获得各种拓扑推断的方法,利用所提出方法的近实时计算能力。所提出方法的优势显著提高了在机器学习工作流中处理大量时间序列数据时的TDA效率和灵活性。

英文摘要:

We propose a novel exact multi-parameter persistent homology method for analyzing time-series data utilizing the Liouville torus. In the field of topological data analysis (TDA), the conventional approach to analyzing time-series data often involves sliding window embedding. From the perspective of Takens' embedding theorem, we justify the analysis of the Liouville torus in TDA and discuss the similarities and differences between the Liouville torus and sliding window embedding approaches. We develop a multi-parameter filtration method based on Fourier decomposition and provide an exact formula of persistent homology with its one-parameter reduction of the multi-parameter filtration. The conventional TDA of time-series data via sliding window is known to be computationally expensive, but the proposed method yields the exact barcode formula with the symmetry of the Liouville torus promptly, which significantly reduces computational complexity while demonstrating comparable or superior performance compared to the existing TDA methods. Furthermore, the proposed method provides a way of obtaining various topological inferences by exploring different filtration rays within the multi-parameter filtration space, utilizing the nearly real-time computational capabilities of the proposed method. The advantages of the proposed method significantly improve the efficiency and flexibility of TDA when handling extensive time-series data within machine learning workflows.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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