Segmentation algorithm for non-stationary compound Poisson processes: With an application to inventory time series of market members in a financial market
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 pro...
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| Format: | Journal article |
| Language: | English |
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2010
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| _version_ | 1797086649502400512 |
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| author | Tóth, B Lillo, F Farmer, J |
| author_facet | Tóth, B Lillo, F Farmer, J |
| author_sort | Tóth, B |
| collection | OXFORD |
| description | 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. © 2010 EDP Sciences, Società Italiana di Fisica, Springer-Verlag. |
| first_indexed | 2024-03-07T02:24:56Z |
| format | Journal article |
| id | oxford-uuid:a544af1d-9c2b-4f6b-acda-e204f41444c0 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:24:56Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:a544af1d-9c2b-4f6b-acda-e204f41444c02022-03-27T02:39:20ZSegmentation algorithm for non-stationary compound Poisson processes: With an application to inventory time series of market members in a financial marketJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a544af1d-9c2b-4f6b-acda-e204f41444c0EnglishSymplectic Elements at Oxford2010Tóth, BLillo, FFarmer, JWe 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. © 2010 EDP Sciences, Società Italiana di Fisica, Springer-Verlag. |
| spellingShingle | Tóth, B Lillo, F Farmer, J Segmentation algorithm for non-stationary compound Poisson processes: With an application to inventory time series of market members in a financial market |
| title | Segmentation algorithm for non-stationary compound Poisson processes: With an application to inventory time series of market members in a financial market |
| title_full | Segmentation algorithm for non-stationary compound Poisson processes: With an application to inventory time series of market members in a financial market |
| title_fullStr | Segmentation algorithm for non-stationary compound Poisson processes: With an application to inventory time series of market members in a financial market |
| title_full_unstemmed | Segmentation algorithm for non-stationary compound Poisson processes: With an application to inventory time series of market members in a financial market |
| title_short | Segmentation algorithm for non-stationary compound Poisson processes: With an application to inventory time series of market members in a financial market |
| title_sort | segmentation algorithm for non stationary compound poisson processes with an application to inventory time series of market members in a financial market |
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