Genetic algorithm with a Bayesian approach for the detection of multiple points of change of time series of counting exceedances of specific thresholds
结合贝叶斯方法的遗传算法：阈值超越计数时间序列的多变点检测AI 解读 ↓
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结合贝叶斯方法的遗传算法：阈值超越计数时间序列的多变点检测
- 研究问题
- 对于记录阈值超越事件的时间序列，如何自动确定多个变点及分段结构，以改善非齐次泊松过程模型的拟合？
- 方法与思路
- 作者将所有超越日期作为候选变点，以日期组合编码遗传算法染色体，并用联系分段参数后验的最小描述长度目标评价。
- 材料陈述的贡献
- 摘要提出自动分段搜索方法，声称可减少对低可能性配置的计算，并通过遗传搜索寻找非局部的最佳变点配置。
- 阅读关注点
- 应核对最小描述长度与联合后验的确切关系、变点数量控制和终止规则，尤其检查所谓最佳解保证是否有严格依据。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收未核实。摘要未展示全局最优证明或数值结果，不能将启发式搜索视为已保证最优。
原文摘要
Although the applications of Non-Homogeneous Poisson Processes to model and study the threshold overshoots of interest in different time series of measurements have proven to provide good results, they needed to be complemented with an efficient and automatic diagnostic technique to establish the location of the change-points, which, when taken into account, make the estimated model fit poorly in regards of the information contained in the real model. For this reason, we propose a new method to solve the segmentation uncertainty of the time series of measurements, where the emission distribution of exceedances of a specific threshold is the focus of investigation. One of the great contributions of the present algorithm is that all the days that overflowed are candidates to be a change-point, so all the possible configurations of overflow days are the possible chromosomes, which will unite to have offspring. Under the heuristics of a genetic algorithm, the solution to the problem of finding such change points will be guaranteed to be non-local and the best possible one, reducing wasted machine time evaluating the least likely chromosomes to be a solution to the problem. The analytical evaluation technique will be by means of the Minimum Description Length (\textit{MDL}) as the objective function, which is the joint posterior distribution function of the parameters of each regime and the change points that determines them and which account as well for the influence of the presence of said times.