R中每周计算平均绝对百分比误差的问题
我有一个每季度的实际和预测发电量的大时间序列，如下所示：R中每周计算平均绝对百分比误差的问题,r,mean,percentage,absolute,R,Mean,Percentage,Absolute,我有一个每季度的实际和预测发电量的大时间序列，如下所示： df<-structure(list(DATETIME = structure(c(1604188800, 1604189700, 1604190600, 1604191500, 1604192400, 1604193300, 1604194200, 1604195100, 1604196000, 1604196900, 1604197800, 1604198700, 1604199600, 1604200500, 1
df<-structure(list(DATETIME = structure(c(1604188800, 1604189700,
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42.225, 42.225, 43.463, 43.463, 43.463, 43.463)), row.names = c(NA,
672L), class = "data.frame")
图书馆（lubridate）
图书馆（magrittr）
df%>%分组依据（等周（日期时间）、年（日期时间））%>%
总结（DateCount=n_distinct（date（DATETIME）），平均误差=平均值（abs（预测实际）/实际））
`isoweek(DATETIME)` `year(DATETIME)` DateCount MeanError
<dbl> <dbl> <int> <dbl>
1 44 2020 1 0.858
2 45 2020 6 0.357
`isoweek（DATETIME）`year（DATETIME）`DateCount平均错误
1 44 2020 1 0.858
2 45 2020 6 0.357
df%
不可抵抗
df%%突变（日期时间=dmy\U hm（日期时间））
df%>%分组依据（年份（日期时间）、等周（日期时间））%>%
总结（DateCount=n_distinct（date（DATETIME）），平均误差=平均值（abs（预测实际）/实际））
`year(DATETIME)` `isoweek(DATETIME)` DateCount MeanError
<dbl> <dbl> <int> <dbl>
1 2020 44 1 0.858
2 2020 45 7 0.321
3 2020 46 7 0.505
4 2020 47 7 0.439
5 2020 48 7 0.309
6 2020 49 7 0.545
7 2020 50 7 0.342
8 2020 51 7 0.357
9 2020 52 7 0.204
10 2020 53 4 0.120
# … with 14 more rows
`年份（日期时间）`isoweek（日期时间）`DateCount平均错误
1 2020 44 1 0.858
2 2020 45 7 0.321
3 2020 46 7 0.505
4 2020 47 7 0.439
5 2020 48 7 0.309
6 2020 49 7 0.545
7 2020 50 7 0.342
8 2020 51 7 0.357
9 2020 52 7 0.204
10 2020 53 4 0.120
#…还有14行
如果设置正确，日期应该可以正常工作。不幸的是，这会为每个星期生成相同的值，似乎不起作用。计算的百分比对应于数据集的整个时间段。它在此处起作用。注意：您的数据包含的数据少于一周的值。我再次运行它，它仍然会为每周生成相同的百分比（我的数据集有22周）。奇怪的是，在结果中，并非所有周的年份都是正确的（虽然我的数据集开始于2020年，但所有周的年份都是2021年）。但是运行上面粘贴的精确代码，您会得到我得到的结果，对吗？您能检查一下：
表（week（your.data$DATETIME））
`year(DATETIME)` `isoweek(DATETIME)` DateCount MeanError
<dbl> <dbl> <int> <dbl>
1 2020 44 1 0.858
2 2020 45 7 0.321
3 2020 46 7 0.505
4 2020 47 7 0.439
5 2020 48 7 0.309
6 2020 49 7 0.545
7 2020 50 7 0.342
8 2020 51 7 0.357
9 2020 52 7 0.204
10 2020 53 4 0.120
# … with 14 more rows