Segmentation algorithm for non-stationary compound Poisson processes
AbstractWe introduce an algorithm for the segmentation of a class of regime switching processes. The segmentation algorithm is a non parametric statistical method able to identify the regimes (patches) of the time series. The process is composed of consecutive patches of variable length, each patch being described by a stationary compound Poisson process, i.e. a Poisson process where each count is associated to a fluctuating signal. The parameters of the process are different in each patch and therefore the time series is non stationary. Our method is a generalization of the algorithm introduced by Bernaola-Galvan, et al., Phys. Rev. Lett., 87, 168105 (2001). We show that the new algorithm outperforms the original one for regime switching compound Poisson processes. As an application we use the algorithm to segment the time series of the inventory of market members of the London Stock Exchange and we observe that our method finds almost three times more patches than the original one.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1001.2549.
Date of creation: Jan 2010
Date of revision: Feb 2011
Publication status: Published in Eur. Phys. J. B 78, 235-243 (2010)
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Web page: http://arxiv.org/
Other versions of this item:
- B. Tóth & F. Lillo & J. D. Farmer, 2010. "Segmentation algorithm for non-stationary compound Poisson processes," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 78(2), pages 235-243, November.
- NEP-ALL-2010-02-05 (All new papers)
- NEP-CMP-2010-02-05 (Computational Economics)
- NEP-ECM-2010-02-05 (Econometrics)
- NEP-ETS-2010-02-05 (Econometric Time Series)
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