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library(ggplot2)
library(dplyr)
library(tsibble)
library(feasts)


structure(list(Date = structure(c(18809, 18810, 18811, 18812, 
18813, 18814, 18815, 18816, 18817, 18818, 18819, 18820, 18821, 
18822, 18823, 18824, 18825, 18826, 18827, 18828, 18829, 18830, 
18831, 18832, 18833, 18834, 18835, 18836, 18837, 18838, 18839, 
18840, 18841, 18842, 18843, 18844, 18845, 18846, 18847, 18848, 
18849, 18850, 18851, 18852, 18853, 18854, 18855, 18856, 18857, 
18858, 18859, 18860, 18861, 18862, 18863, 18864, 18865, 18866, 
18867, 18868, 18869, 18870, 18871, 18872, 18873, 18874, 18875, 
18876, 18877, 18878, 18879, 18880, 18881, 18882, 18883, 18884, 
18885), class = "Date"), Cost.per.Complete.Payment = c(165.255, 
121.418, 124.69, 151.7425, 155.268, 162.961666666667, 143.01, 
172.854, 292.742, 104.88, 87.98, 132.7025, 73.865, 117.258, 187.297777777778, 
186.31625, 194.785, 124.1825, 180.443333333333, 193.347777777778, 
200.788888888889, 172.950909090909, 175.171666666667, 244.03125, 
208.395, 135.334444444444, 188.028, 185.668571428571, 339.939411764706, 
207.62, 259.25, 197.46, 175.341666666667, 183.813333333333, 194.3525, 
93.2125, 125.871428571429, 121.939166666667, 123.983, 267.26, 
134.28375, NA, 324.1675, 188.22, 132.623333333333, 76.8233333333333, 
110.8, 104.606666666667, 102.306666666667, 187.265, 349.498333333333, 
215.255, 188.11, 246.963333333333, 129.074, 88.59, 113.315, 115.1, 
103.546, 108.793333333333, 153.595, 180.83, 144.372, 120.108, 
148.9175, 342.283333333333, 318.5575, 150.116, 197.5525, 100.046666666667, 
85.4525, 127.635, 177.522, 135.4375, 77.0316666666667, 123.998, 
270.258)), row.names = c(NA, -77L), key = structure(list(.rows = structure(list(
    1:77), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", 
"list"))), row.names = c(NA, -1L), class = c("tbl_df", "tbl", 
"data.frame")), index = structure("Date", ordered = TRUE), index2 = "Date", interval = structure(list(
    year = 0, quarter = 0, month = 0, week = 0, day = 1, hour = 0, 
    minute = 0, second = 0, millisecond = 0, microsecond = 0, 
    nanosecond = 0, unit = 0), .regular = TRUE, class = c("interval", 
"vctrs_rcrd", "vctrs_vctr")), class = c("tbl_ts", "tbl_df", "tbl", 
"data.frame"))

我转换为 tribble 对象：

data_frame <- as_tsibble(data_frame)

然后我尝试运行这段代码：

data_frame %>%
  model(
    STL(Cost.per.Complete.Payment ~ trend(window = 7) +
          season(window = 'periodic'),
        robust = TRUE)) %>%
  components() %>%
  autoplot()

但我得到一个错误。

1 error encountered for STL(Cost.per.Complete.Payment ~ trend(window = 7) + season(window = "periodic"), 
    robust = TRUE)
[1] STL decomposition does not support series with missing values.
Error: Problem with `mutate()` column `cmp`.
ℹ `cmp = map(.fit, components)`.
x no applicable method for 'components' applied to an object of class "null_mdl"

我不确定这意味着什么/我非常困惑为什么它说时间序列中存在缺失值。当我检查唯一日期值的数量时，我得到 77，当我从最小日期中减去最大日期时，我得到比 77 少 1，即 76。所以我觉得检查出来了（另外我手动检查了日期列以查看如果有缺失值并且没有）。

length(unique(data_frame$Date)) # this is 77

max(data_frame$Date) - min(data_frame$Date) # this is 76
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