Modeling dynamic substate chains among massive states
Modeling dynamic substate chains among massive states
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DOI：
10.3233/ida-2008-12303
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
V. Nguyen;T. Washio
中科院分区：
文献类型：
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作者：
V. Nguyen;T. Washio
本文提出了一种新的框架，命名为高阶子状态链（HISC）建模，捕捉整个系统的动态交易时间序列，其中的交易包含爆炸状态，由于大量观察到的输入的组合。在实际情况中，目标系统由多个子系统组成，其中每个子系统的状态由事务的子集表示。因此，从整个目标系统观察到的事务被认为是这些子集的集合，并且每个子集被称为目标系统的“子状态”。我们的HISC建模的基本任务是有效地，同时确定嵌入在时间序列中的子状态及其转换。为便于应用，提出了利用HISC模型进行系统动力学仿真和子状态预测的方法。通过对合成数据的评价、与现有高阶马尔可夫链模型的比较以及在实际数据分析中的应用，证实了该方法的有效性。
This paper proposes a novel framework, named HIgh-order Substate Chain (HISC) modeling, to capture the entire system dynamics underlying the transaction time series, where the transaction contains explosive states due to the combinatorics of massively observed inputs. In a practical situation, the objective system consists of multiple subsystems where a state of each subsystem is represented by a subset of the transaction. Thus, a transaction observed from the entire objective system is considered to be a collection of such subsets, and each subset is called a "substate" of the objective system. The basic task of our HISC modeling is to efficiently and simultaneously identify the substates and their transitions embedded in the time series. For application, the methods for system dynamics simulation and substate prediction by using the HISC model have been developed. Its significant performance has been confirmed through the evaluation on synthetic data, the comparisons with some High-order Markov chain models in the state of the art and the application to practical data analysis.