【摘要】 利用离散小波变换对随机过程或时间序列进行多尺度分析,在多尺度空间中研究时间序列的方差及性质,利用小波方差的对数近似地线性依赖尺度对数这一特性,将最小二乘估计方法应用到长记忆过程参数估计问题中,从而提出长记忆过程的多尺度最小二乘估计的新方法.利用此方法不但能降低对随机参数估计时的计算量,而且在精度上也可达到令人满意的结果.更多还原

【Abstract】 This paper firstly uses discrete wavelet transform to process stochastic processes or time series on the analysis of multiscale space,then studies the variance and statistical properties of the series at different scales.Subsequently,it applies Least Squares Estimation to parameter estimation according to the property that log variance is approximately simple linear equation of log scale,finally a new method named multiscale Least Squares Estimation is put forward.The paper proposes that the new algorithm can effectively decrease computation complexity and obtain satisfying estimation precision.更多还原