June 2020
Beginner to intermediate
522 pages
9h 6m
Chinese
Content preview from R 语言经典实例（原书第 2 版）
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436
｜
第
14
章
而不能知道哪一个相关系数显著。
p
值为 0.8 表示没有检测到第二个时间序列中的自相关。
Box.test 函数还可以执行 Ljung-Box 检验，对于小样本执行该检验较好。该检验计算
一个
p
值，其解释与 Box-Pierce 检验
p
值的解释相同：
Box.test(ts, type = "Ljung-Box")
14.14.4 另请参阅
参见 14.13 节以绘制自相关函数，它可以可视化地查看自相关函数。
14.15 绘制偏自相关函数
14.15.1 问题
绘制时间序列的偏自相关函数（Partial AutoCorrelation Function，PACF）。
14.15.2 解决方案
使用 pacf 函数：
pacf(
ts
)
14.15.3 讨论
偏自相关函数是另一种用于揭示时间序列中内部关系的工具。然而，它的解释远不如自
相关函数的解释来的直观。我们将偏相关的数学定义留给统计学教科书。在这里，我们
只是说两个随机变量
X
和
Y
之间的偏相关是在剔除了由于其他变量导致的
X
和
Y
的相关
性后剩余的
X
和
Y
的相关性。对于时间序列而言，
k
阶偏自相关函数是考虑相隔
k
步的
数据点，在剔除了这
k
步之间的数据导致的相关性后的相关性。
偏自相关函数可以帮助识别 ARIMA 模型中的自回归（AutoRegression，AR）系数的个
数。以下示例显示了 14.13 节中使用的两个时间序列的 PACF。其中一个序列具有偏自
相关性，而另一个则没有。超出虚线的偏自相关函数在统计上是显著的。在第一个时间
序列（见图 14-7 ...
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