Exact multi-parameter persistent homology of time-series data: one-dimensional reduction of multi-parameter persistence theory

In various applications of data classification and clustering problems, multi-parameter analysis is effective and crucial because data are usually defined in multi-parametric space. Multi-parameter persistent homology, an extension of persistent homology of one-parameter data analysis, has been developed for topological data analysis (TDA). Although it is conceptually attractive, multi-parameter persistent homology still has challenges in theory and practical applications. In this study, we consider time-series data and its classification and clustering problems using multi-parameter persistent homology. We develop a multi-parameter filtration method based on Fourier decomposition and provide an exact formula and its interpretation of one-dimensional reduction of multi-parameter persistent homology. The exact formula implies that the one-dimensional reduction of multi-parameter persistent homology of the given time-series data is equivalent to choosing diagonal ray (standard ray) in the multi-parameter filtration space. For this, we first consider the continuousization of time-series data based on Fourier decomposition towards the construction of the exact persistent barcode formula for the Vietoris-Rips complex of the point cloud generated by sliding window embedding. The proposed method is highly efficient even if the sliding window embedding dimension and the length of time-series data are large because the method precomputes the exact barcode and the computational complexity is as low as the fast Fourier transformation of $O(N \log N)$. Further the proposed method provides a way of finding different topological inferences by trying different rays in the filtration space in no time.Comment: 29 page

A multi-domain hybrid method for head-on collision of black holes in particle limit

A hybrid method is developed based on the spectral and finite-difference methods for solving the inhomogeneous Zerilli equation in time-domain. The developed hybrid method decomposes the domain into the spectral and finite-difference domains. The singular source term is located in the spectral domain while the solution in the region without the singular term is approximated by the higher-order finite-difference method. The spectral domain is also split into multi-domains and the finite-difference domain is placed as the boundary domain. Due to the global nature of the spectral method, a multi-domain method composed of the spectral domains only does not yield the proper power-law decay unless the range of the computational domain is large. The finite-difference domain helps reduce boundary effects due to the truncation of the computational domain. The multi-domain approach with the finite-difference boundary domain method reduces the computational costs significantly and also yields the proper power-law decay. Stable and accurate interface conditions between the finite-difference and spectral domains and the spectral and spectral domains are derived. For the singular source term, we use both the Gaussian model with various values of full width at half maximum and a localized discrete $\delta$-function. The discrete $\delta$-function was generalized to adopt the Gauss-Lobatto collocation points of the spectral domain. The gravitational waveforms are measured. Numerical results show that the developed hybrid method accurately yields the quasi-normal modes and the power-law decay profile. The numerical results also show that the power-law decay profile is less sensitive to the shape of the regularized $\delta$-function for the Gaussian model than expected. The Gaussian model also yields better results than the localized discrete $\delta$-function.Comment: 25 pages; published version (IJMPC