The European Physical Journal B

, Volume 78, Issue 2, pp 235–243 | Cite as

Segmentation algorithm for non-stationary compound Poisson processes

With an application to inventory time series of market members in a financial market
  • B. Tóth
  • F. Lillo
  • J. D. Farmer


We 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 a time series. The process is composed of consecutive patches of variable length. In each patch the process is described by a stationary compound Poisson process, i.e. a Poisson process where each count is associated with 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-Galván, et al. [Phys. Rev. Lett. 87, 168105 (2001)]. We show that the new algorithm outperforms the original one for regime switching models of 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.


Segmentation Algorithm Regime Switching Jaccard Index Compound Poisson Process Jump Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Santa Fe InstituteSanta FeUSA
  2. 2.Dipartimento di Fisica e Tecnologie RelativeUniversitá di PalermoPalermoItaly
  3. 3.LUISS Guido Carli, Viale Pola 12RomaItaly

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