基于HTM的遗传时间序列分割算法

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。

HTM-based Genetic Time Series Segmentation Algorithm

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.