计算机与现代化 ›› 2014, Vol. 0 ›› Issue (10): 112-118.doi: 10.3969/j.issn.1006-2475.2014.10.026

• 算法设计与分析 • 上一篇    下一篇

基于HTM的遗传时间序列分割算法

  

  1. 湄洲湾港口管理局,福建泉州362001
  • 收稿日期:2014-07-08 出版日期:2014-11-03 发布日期:2014-11-05
  • 作者简介:吴大华(1986-),男,福建泉港人,湄洲湾港口管理局助理工程师,硕士,研究方向:机器学习。

HTM-based Genetic Time Series Segmentation Algorithm

  1. Meizhou Bay Port Administrative Bureau, Quanzhou 362001, China
  • Received:2014-07-08 Online:2014-11-03 Published:2014-11-05

摘要: 结合层级实时记忆(Hierarchical Temporal Memory,HTM)模型与基于模式集的遗传时间序列分割算法各自的优点,用基于HTM的适应值函数替换原基于模式集的适应值函数,提出基于HTM的遗传时间序列分割算法。该算法可实现时间序列的分割及其相应子序列的分类识别。同时,针对HTM对训练样本的要求,提出一种基于模式集的HTM训练样本生成算法。最后在股票序列上验证了这2种算法的有效性。

关键词: 时间序列, 分割, 层级实时记忆, 遗传算法

Abstract: This paper proposes a time series segmentation approach by combining the advantages of hierarchical temporal memory (HTM) model and the pattern-based genetic time series segmentation algorithm. The approach is a HTM-based genetic algorithm which replaces the pattern-based fitness value function with the HTM-based fitness value function. The approach can be applied to find segments from a time series and to identify the class of sub series. In addition, a pattern-based algorithm of HTM sample generation is proposed for generating sample set with HTM trait. Experimental results show that the two algorithms are effective on the time series of stocks.

Key words: time series, segmentation, HTM(Hierarchical Temporal Memory), genetic algorithm

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