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IJMLC 2020 Vol.10(6): 740-745 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.6.999

Switching Probabilistic Slow Feature Analysis for Time Series Data

Kazuki Tsujimoto and Toshiaki Omori

Abstract—Slow feature analysis (SFA) is a machine learning method for extracting slowly time-varying feature from multi-dimensional time series data. Recently, probabilistic SFA (PSFA) that extends SFA to a probabilistic framework has been proposed. The PSFA can be applied to stationary time series data with noise and missing values. In order to deal with non-stationary time series data including change points, we propose a switching probabilistic slow feature analysis (switching PSFA) in this paper. By introducing a switching state space model, it is possible to extract slowly varying information even when system parameters change with time. Using the proposed method, we show that slowly time-varying components can be extracted more accurately from time-series data with non-stationarity.

Index Terms—Slow feature analysis, switching state space model, Bayesian time series analysis, statistical machine learning.

The authors are with the Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501 Japan (e-mail: omori@eedept.kobe-u.ac.jp).


Cite: Kazuki Tsujimoto and Toshiaki Omori, "Switching Probabilistic Slow Feature Analysis for Time Series Data," International Journal of Machine Learning and Computing vol. 10, no. 6, pp. 740-745, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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