﻿ 似然比扫描法在长记忆时间序列均值多变点中的应用 Application of Likelihood Ratio Scanning Method in Multiple Mean Changes in Long Memory Time Series

Hans Journal of Data Mining
Vol. 09  No. 02 ( 2019 ), Article ID: 28838 , 9 pages
10.12677/HJDM.2019.92002

Application of Likelihood Ratio Scanning Method in Multiple Mean Changes in Long Memory Time Series

Qiongyao Xu, Yuhong Xing

School of Mathematics and Statistics, Qinghai Normal University, Xining Qinghai

Received: Jan. 23rd, 2019; accepted: Feb. 6th, 2019; published: Feb. 13th, 2019

ABSTRACT

Based on likelihood ratio scanning method (LRSM), this paper studies the multiple change point problem of mean in piecewise stationary long memory time series. Through numerical simulation, it is found that applying the LRSM directly to long memory time series will lead to inaccurate detection of the number and location of change points. By revising the residual estimation method of likelihood function parameters in LRSM, a new LRSM is proposed which is suitable for long memory time series. The effectiveness and practicability of the improved method are proved by numerical simulation and actual data analysis.

Keywords:Long Memory Time Series, Mean Change Points, Likelihood Ratio Scanning Statistics

Copyright © 2019 by author(s) and Hans Publishers Inc.

1. 引言

2. 似然比扫描方法

2.1. 基本设定和假设

${Y}_{t,j}={X}_{t}$ , ${\tau }_{j-1} ,

${Y}_{t,j}={\varphi }_{j0}+{\varphi }_{j1}{Y}_{t-1,j}+\cdots +{\varphi }_{j,{p}_{j}}{Y}_{t-{p}_{j},j}+{\sigma }_{j}{\epsilon }_{t}$

${\epsilon }_{t}$ 为独立同分布的白噪声序列，且均值为0，方差为1。

2.2. 用似然扫描方法估计变点的三个步骤

${W}_{t}\left(h\right)=\left\{t-h+1,\cdots ,t+h\right\}$ ,

${X}_{{W}_{t}\left(h\right)}=\left({X}_{t-h+1},\cdots ,{X}_{t+h}\right)$

$L\left(\theta \right)=\sum _{t=1}^{n}{l}_{t}\left(\theta \right)\equiv \sum _{t=1}^{n}\mathrm{log}\left\{{f}_{\theta }\left({z}_{t}/{z}_{t-1},{z}_{t-2},\cdots ,{z}_{t-p}\right)\right\}$ (1)

${S}_{h}\left(t\right)=\frac{1}{h}{L}_{1h}\left(t,{\stackrel{^}{\theta }}_{1}\right)+\frac{1}{h}{L}_{2h}\left(t,{\stackrel{^}{\theta }}_{2}\right)-\frac{1}{h}{L}_{\cdot h}\left(t,\stackrel{^}{\theta }\right)$

${S}_{h}\left(t\right)$ 统计量扫描所有的观测值可以得到一系列的似然比扫描统计量

$\left({S}_{h}\left(h\right),{S}_{h}\left(h+1\right),\cdots ,{S}_{h}\left(n-h\right)\right)$ 。如果t是变点，那么 ${S}_{h}\left(t\right)$ 的值会趋向于变大，由于选择的窗口长度为2h，

${\stackrel{^}{J}}^{\left(1\right)}=\left\{m\in \left\{h,h+1,\cdots ,n-h\right\}:{S}_{h}\left(m\right)=\underset{t\in \left(m-h,m+h\right)}{\mathrm{max}}{S}_{h}\left(t\right)\right\}$

$t 或者是 $t>n-h$ 时， ${S}_{t}\left(h\right)=0$ ，如果 ${S}_{h}\left(m\right)$ 在以点m为中心的窗口 $\left[m-h+1,m+h\right]$ 中达到最

$\text{MDL}\left(m,J,p\right)=\mathrm{log}\left(m\right)+\left(m+1\right)\mathrm{log}\left(n\right)+\sum _{j=1}^{m+1}\mathrm{log}\left({p}_{j}\right)+\sum _{j=1}^{m+1}\frac{{p}_{j}+2}{2}\mathrm{log}\left({n}_{j}\right)-\sum _{j=1}^{m+1}{L}_{j}\left({\stackrel{^}{\theta }}_{j}\right)$

$\left({\stackrel{^}{m}}^{\left(2\right)},{\stackrel{^}{J}}^{\left(2\right)},{\stackrel{^}{p}}^{\left(2\right)}\right)=\underset{\begin{array}{l}m=|J|,J\in {\stackrel{^}{J}}^{\left(1\right)}\\ p\in {\left\{1,\cdots ,{p}_{\mathrm{max}}\right\}}^{m}\end{array}}{\mathrm{arg}\mathrm{min}}\text{MDL}\left(m,J,p\right)$

${E}_{j}\left(h\right)=\left\{{\stackrel{^}{\tau }}_{j}^{\left(2\right)}-2h-1,\cdots ,{\stackrel{^}{\tau }}_{j}^{\left(2\right)}+2h\right\}$

${X}_{{E}_{j}\left(h\right)}=\left({X}_{{\stackrel{^}{\tau }}_{j}^{\left(2\right)}-2h-1},\cdots ,{X}_{{\stackrel{^}{\tau }}_{j}^{\left(2\right)}+2h}\right)$

${L}_{j}\left(\tau ,{\theta }_{1},{\theta }_{2}\right)=\sum _{t={\stackrel{^}{\tau }}_{j}^{\left(2\right)}-2h+1}^{\tau }{l}_{t}\left({\theta }_{1}\right)+\sum _{t=\tau +1}^{{\stackrel{^}{\tau }}_{j}^{\left(2\right)}+2h}{l}_{t}\left({\theta }_{2}\right)$ ，对于 $j=1,\cdots ,{\stackrel{^}{m}}^{\left(2\right)}$ ，定义最后的变点估计量为：

${\stackrel{^}{\tau }}_{j}^{\left(3\right)}=\mathrm{arg}\underset{\tau \in \left({\stackrel{^}{\tau }}_{j}^{\left(2\right)}-h,{\stackrel{^}{\tau }}_{j}^{\left(2\right)}+h\right]}{\mathrm{max}}{L}_{j}\left(\tau ,{\stackrel{^}{\theta }}_{j},{\stackrel{^}{\theta }}_{j+1}\right)$

3. 对似然比扫描方法的改进

${\left(1-L\right)}^{{d}_{0}}{X}_{t}={\epsilon }_{t}$ , $t=1,2,\cdots ,n$

4. 数值模拟

Table 1. Correctly detect the number of times that there is no change point

Table 2. Correctly detect the number, position and error of one change point

Table 3. Correctly detect the number, position and error of two change points

5. 实例分析

Figure 1. Yield of Shanghai composite index on January 2, 1992 solstice on December 29, 2000

6. 小结

Application of Likelihood Ratio Scanning Method in Multiple Mean Changes in Long Memory Time Series[J]. 数据挖掘, 2019, 09(02): 9-17. https://doi.org/10.12677/HJDM.2019.92002

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