A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Stationary subspace analysis based on second-order statistics (2024)
Flumian, L., Matilainen, M., Nordhausen, K., & Taskinen, S. (2024). Stationary subspace analysis based on second-order statistics. Journal of Computational and Applied Mathematics, 436, Article 115379. https://doi.org/10.1016/j.cam.2023.115379
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Flumian, Lea; Matilainen, Markus; Nordhausen, Klaus; Taskinen, Sara
Lehti tai sarja: Journal of Computational and Applied Mathematics
ISSN: 0377-0427
eISSN: 1879-1778
Julkaisuvuosi: 2024
Ilmestymispäivä: 10.06.2023
Volyymi: 436
Artikkelinumero: 115379
Kustantaja: Elsevier BV
Julkaisumaa: Belgia
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.cam.2023.115379
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava
Rinnakkaistallenteen verkko-osoite (pre-print): https://arxiv.org/abs/2103.06148
Tiivistelmä
In stationary subspace analysis (SSA) one assumes that the observable p-variate time series is a linear mixture of a k-variate nonstationary time series and a (p – k)-variate stationary time series. The aim is then to estimate the unmixing matrix which transforms the observed multivariate time series onto stationary and nonstationary components. In the classical approach multivariate data are projected onto stationary and nonstationary subspaces by minimizing a Kullback–Leibler divergence between Gaussian distributions, and the method only detects nonstationarities in the first two moments. In this paper we consider SSA in a more general multivariate time series setting and propose SSA methods which are able to detect nonstationarities in mean, variance and autocorrelation, or in all of them. Simulation studies illustrate the performances of proposed methods, and it is shown that especially the method that detects all three types of nonstationarities performs well in various time series settings. The paper is concluded with an illustrative example.
YSO-asiasanat: aikasarjat; aikasarja-analyysi; monimuuttujamenetelmät
Vapaat asiasanat: autocorrelation; joint diagonalization; multivariate time series; second-order stationary; supervised dimension reduction
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2024
Alustava JUFO-taso: 1
