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中文摘要：
以广东省东江流域月降雨序列为例，在介绍相空间重构原理的基础上，探讨了混沌分析的主要定量指标：饱和关联维数D：和最大Lyapunov指数λ。得到该时间序列的饱和关联维数D2=3．93，最小嵌入维数m=8，最佳嵌入滞时τ=3个月，最大Lyapunov指数λ=0．253。并且采用主分量方法进一步验证了该序列具有混沌特性，指出该序列的预测时限不应超过4个月，对此结论则用ARMA（p，q）模型作了验证，为东江流域月降雨预测提供了较为科学的依据。
英文摘要：
Based on introducing the phase space reconstruction theory of chaotic time series, the main quantitative indexes of saturation correlation dimension D2 and maximal Lyapunov exponent A for chaotic analysis are discussed with the monthly rainfall time series of the Dongjiang River Valley in Guangdong province. The saturation correlation dimension, minimum embedding dimension, optimal built-in delay time and maximal Lyapunov exponent are calculated and given, that is D2 = 3.93, m = 8, τ = 3 and λ = 0. 253. On the basis of this, primary component analysis method is applied to validate its chaotic character. It is suggested that the forecasting length for this rainfall time serial should not exceed 4 months and this is verified with the model of ARMA. This time series chaotic analy- sis will provide a scientific gist for monthly rainfall forecasting in the Dongjiang River Valley.
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