The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of CIRCUITS, SYSTEMS and SIGNAL PROCESSING
Transactions ID Number: 19-522
Full Name: Terumasa Tokunaga
Position: Student
Age: ON
Sex: Male
Address: 33 Kyushu University 6-10-1 Hakozaki, Fukuoka
Country: JAPAN
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E-mail address: tokunaga@geo.kyushu-u.ac.jp
Other E-mails: terru@mac.com
Title of the Paper: Onset Time Determination of Precursory Events in Time Series Data by an Extension of Singular Spectrum Transformation
Authors as they appear in the Paper: Terumasa Tokunaga, Daisuke Ikeda, Kazuyuki Nakamura, Tomoyuki Higuchi, Akimasa Yoshikawa, Teiji Uozumi, Akiko Fujimoto, Akira Morioka, Kiyohumi Yumoto, and CPMN group
Email addresses of all the authors: tokunaga@geo.kyushu-u.ac.jp,daisuke@inf.kyushu-u.ac.jp,knaka@isc.meiji.ac.jp,higuchi@ism.ac.jp,yoshi@geo.kyushu-u.ac.jp,uozumi@serc.kyushu-u.ac.jp,akiko@stp.isas.jaxa.jp,morioka@pparc.geophys.tohoku.ac.jp,yumoto@serc.kyushu-u.ac.jp
Number of paper pages: 15
Abstract: To predict an occurrence of extraordinary phenomena, such as earthquakes, failures of engineering systems and financial market crushes, it is important to identify precursory events in time series. However, existing methods are limited in their applicability for real world precursor detections. Recently, Ide and Inoue [2005] have developed an SSA-based change-point detection method, called singular spectrum transformation (SST). SST is suitable for detecting various types of change-points, but real world precursor detections can be far more difficult than expected. In general, precursory events are observed as minute and less-visible fluctuations preceding an onset of massive fluctuations of extraordinary phenomena and therefore they are easily over-looked. To overcome this point, we extend the conventional SST to the multivariable SST. The originality of our strategy is in focusing on synchronism detections of precursory events in multiple sequences of univariate !
time series. We performed some experiments by using artificial data and showed the superiority of multivariable SST in detecting onset of precursory events. Furthermore, the superiority is also shown statistically in determining the onset of precursory events by using real world time series.
Keywords: signal processing,time series analysis,singular spectrum analysis,singular spectrum transformation, precursor detection, change-point detection
EXTENSION of the file: .pdf
Special (Invited) Session: detecting precursory events in time series data by an extension of singular spectrum transformation
Organizer of the Session: 635-393
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