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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时间序列分析
｜
437
Significant Partial Autocorrelations
Partial ACF
0.0
0.2
0.4
0.6
5 10 15 20
Lag
图 14-7：
ts1
的偏自相关系数
Insignificant Partial Autocorrelations
Partial ACF
-
0.10
0.00
0.10
5 10 15 20
Lag
图 14-8：
ts2
的偏自相关系数
14.15.4 另请参阅
参见 14.13 节。
14.16 两个时间序列间的滞后相关性
14.16.1 问题
现有两个时间序列，二者之间是否存在某种滞后相关性？
14.16.2 解决方案
使用 forecast 包中的 Ccf 函数绘制交叉相关函数，这将揭示滞后相关性：
438
｜
第
14
章
library(forecast)
Ccf(
ts1
,
ts2
)
14.16.3 讨论
交叉相关函数帮助揭示两个时间序列之间的滞后相关性。当一个时间序列今天的值与另
一个时间序列未来或过去的值相关时，就会出现滞后相关性。
考虑商品价格和债券价格之间的关系。一些分析师认为，这些价格是相互关联的，因为
商品价格的变化是通货膨胀的一个晴雨表，而通货膨胀是债券定价的关键因素之一。我
们能发现它们之间的相关性吗？
图 14-9 ...
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