时间序列的符号化方法研究

Study on Symbolization Analysis of Time Series

符号化是一种重要的时间序列分析方法,但是如何选择合适的符号化策略却是一个难题.有限统计复杂性表达符号序列中包含的信息量,它可以作为符号化处理的评价标准.本文首先分析现有的符号化方法,如:静态法、动态法、小波空间法等.然后选用8组时间序列为例,用不同符号化方法处理它们,计算并比较符号序列的有限统计复杂性.因为8组时间序列分别来自不同领域,且都是非线性和非平稳的,因此分析结果会导出一些有意义的经验结论.综合评价认为:动态法是符号化方法的首选,其次是综合法和小波空间法,最常用的静态法效果反而最差.

Symbolization is an important method for time series analysis, but choosing appropriate symbolization strategy is very difficult. Finite statistic complexity （FSC） can calculate the information quantity contained in the symbol time series, so it is evaluation criterion of symbolizing process. In this paper, several symbolization methods are analyzed including static transformation method, dynamic method, wavelet space method, etc. Eight time series are transformed into the symbol series by different methods and the FSC of all the symbol series are compared from several aspects. These time series which come from different domains are nonlinear and nonstationary. Some meaningful empirical conclusions are thus drawn. All of the analyses imply that the dynamic transformation is the best, and then the integrated one and wavelet space one. Unexpectedly, the static transformation is the most commonly used but the worst.