摘要翻译：
本文介绍了一种分割一类区域切换过程的算法。分割算法是一种非参数统计方法，能够识别时间序列的状态（斑块）。该过程由可变长度的连续贴片组成，每个贴片由平稳复合泊松过程描述，即泊松过程，其中每个计数与波动信号相关联。过程的参数在每个斑块中是不同的，因此时间序列是非平稳的。我们的方法是Bernaola-Galvan等人提出的算法的推广。Rev.Lett.，87，168105(2001)。我们证明了新算法在区域切换复合泊松过程中的性能优于原算法。作为一个应用，我们用该算法对伦敦证券交易所市场成员库存的时间序列进行分割，我们发现我们的方法比原来的方法找到了几乎三倍多的补丁。
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英文标题：
《Segmentation algorithm for non-stationary compound Poisson processes》
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作者：
Bence Toth, Fabrizio Lillo, J. Doyne Farmer
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最新提交年份：
2011
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分类信息：

一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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英文摘要：
  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 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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PDF链接：
https://arxiv.org/pdf/1001.2549