在 R 中每周计算平均绝对百分比误差的问题
problem with calculating mean absolute percentage error on a weekly basis in R
我有大量时间序列的每个刻钟的实际和预测发电值，如下所示：
df<-structure(list(DATETIME = structure(c(1604188800, 1604189700,
1604190600, 1604191500, 1604192400, 1604193300, 1604194200, 1604195100,
1604196000, 1604196900, 1604197800, 1604198700, 1604199600, 1604200500,
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1604784600, 1604785500, 1604786400, 1604787300, 1604788200, 1604789100,
1604790000, 1604790900, 1604791800, 1604792700), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), Actual = c(5.718, 5.844, 5.971, 5.925,
5.414, 5.432, 5.521, 5.513, 5.524, 6.182, 6.947, 7.133, 7.746,
7.058, 6.615, 9.665, 12.015, 12.029, 11.957, 12.981, 16.03, 16.821,
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13.399, 11.806, 10.78, 9.962, 10.093, 8.922, 10.099, 9.832, 8.406,
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5.892, 5.148, 4.533, 4.223, 3.89, 3.498, 2.941, 3.06, 3.244,
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3.126, 3.389, 3.205, 3.39, 3.942, 4.029, 4.186, 4.282, 4.335,
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3.579, 3.886, 3.145, 2.984, 3.305, 3.535, 3.59, 4.079, 4.141,
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48.18, 48.067, 47.608, 46.76, 49.739, 47.404, 47.416, 44.787,
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54.747, 53.934, 55.108, 55.669, 57.554, 57.824, 56.961, 54.605,
54.705, 54.599, 54.044, 53.627), Forecasted = c(4.843, 4.843,
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6.285, 6.285, 6.285, 6.285, 6.803, 6.803, 6.803, 6.803, 7.54,
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5.98, 5.98, 5.22, 5.22, 5.22, 5.22, 5.023, 5.023, 5.023, 5.023,
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5.787, 5.787, 5.787, 5.097, 5.097, 5.097, 5.097, 6.878, 6.878,
6.878, 6.878, 6.337, 6.337, 6.337, 6.337, 4.025, 4.025, 4.025,
4.025, 2.665, 2.665, 2.665, 2.665, 4.573, 4.573, 4.573, 4.573,
6.248, 6.248, 6.248, 6.248, 5.534, 5.534, 5.534, 5.534, 6.268,
6.268, 6.268, 6.268, 5.964, 5.964, 5.964, 5.964, 5.23, 5.23,
5.23, 5.23, 7.155, 7.155, 7.155, 7.155, 9.775, 9.775, 9.775,
9.775, 12.565, 12.565, 12.565, 12.565, 11.868, 11.868, 11.868,
11.868, 11.473, 11.473, 11.473, 11.473, 10.698, 10.698, 10.698,
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13.148, 12.845, 12.845, 12.845, 12.845, 11.588, 11.588, 11.588,
11.588, 16.374, 16.374, 16.374, 16.374, 19.543, 19.543, 19.543,
19.543, 21.938, 21.938, 21.938, 21.938, 22.425, 22.425, 22.425,
22.425, 22.738, 22.738, 22.738, 22.738, 28.695, 28.695, 28.695,
28.695, 29.352, 29.352, 29.352, 29.352, 34.76, 34.76, 34.76,
34.76, 31.81, 31.81, 31.81, 31.81, 33.791, 33.791, 33.791, 33.791,
35.23, 35.23, 35.23, 35.23, 35.308, 35.308, 35.308, 35.308, 35.13,
35.13, 35.13, 35.13, 37.11, 37.11, 37.11, 37.11, 37.073, 37.073,
37.073, 37.073, 40.596, 40.596, 40.596, 40.596, 40.7, 40.7, 40.7,
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43.892, 44.33, 44.33, 44.33, 44.33, 42.638, 42.638, 42.638, 42.638,
45.95, 45.95, 45.95, 45.95, 50.997, 50.997, 50.997, 50.997, 50.458,
50.458, 50.458, 50.458, 49.61, 49.61, 49.61, 49.61, 50.95, 50.95,
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34.306, 34.306, 37.668, 37.668, 37.668, 37.668, 41.563, 41.563,
41.563, 41.563, 41.171, 41.171, 41.171, 41.171, 42.225, 42.225,
42.225, 42.225, 43.463, 43.463, 43.463, 43.463)), row.names = c(NA,
672L), class = "data.frame")
我想计算数据集每个（日历）周的平均绝对百分比误差，我尝试了几种方法，例如函数 apply.weekly，但其中 none 似乎工作。
apply.weekly(df, function(x) mean(abs((df$Actual-df$Forecasted)/df$Actual)))
我一直在为数据集的每个季度获取相同的值作为结果，而不是每周的结果。
你对如何实现这个计算有什么想法吗？
预先感谢您的帮助。
也许是这样：
library(lubridate)
library(magrittr)
df %>% group_by( isoweek( DATETIME ), year( DATETIME ) ) %>%
summarise( DateCount = n_distinct(date(DATETIME)), MeanError = mean( abs(Forecasted-Actual)/Actual ) )
它输出：
`isoweek(DATETIME)` `year(DATETIME)` DateCount MeanError
<dbl> <dbl> <int> <dbl>
1 44 2020 1 0.858
2 45 2020 6 0.357
使用更新后的数据源：
df <- read.delim( "https://raw.githubusercontent.com/Argiro1983/data-set/main/test1", sep=";" ) %>%
as_tibble
df %<>% mutate( DATETIME = dmy_hm(DATETIME) )
df %>% group_by( year( DATETIME ), isoweek( DATETIME ) ) %>%
summarise( DateCount = n_distinct(date(DATETIME)), MeanError = mean( abs(Forecasted-Actual)/Actual ) )
输出：
`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
如果您设置正确，日期应该可以正常工作。
我有大量时间序列的每个刻钟的实际和预测发电值，如下所示：
df<-structure(list(DATETIME = structure(c(1604188800, 1604189700,
1604190600, 1604191500, 1604192400, 1604193300, 1604194200, 1604195100,
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1604352600, 1604353500, 1604354400, 1604355300, 1604356200, 1604357100,
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1604784600, 1604785500, 1604786400, 1604787300, 1604788200, 1604789100,
1604790000, 1604790900, 1604791800, 1604792700), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), Actual = c(5.718, 5.844, 5.971, 5.925,
5.414, 5.432, 5.521, 5.513, 5.524, 6.182, 6.947, 7.133, 7.746,
7.058, 6.615, 9.665, 12.015, 12.029, 11.957, 12.981, 16.03, 16.821,
16.897, 15.383, 14.029, 14.642, 10.721, 9.187, 8.466, 6.89, 5.916,
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13.399, 11.806, 10.78, 9.962, 10.093, 8.922, 10.099, 9.832, 8.406,
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7.239, 7.768, 7.338, 7.11, 6.975, 7.35, 6.209, 6.441, 6.641,
5.892, 5.148, 4.533, 4.223, 3.89, 3.498, 2.941, 3.06, 3.244,
3.521, 3.703, 3.314, 3.507, 3.705, 3.073, 2.472, 2.344, 2.695,
2.729, 2.652, 2.582, 2.731, 2.759, 2.866, 3.283, 3.138, 3.009,
3.126, 3.389, 3.205, 3.39, 3.942, 4.029, 4.186, 4.282, 4.335,
4.026, 3.863, 3.772, 3.25, 3.282, 3.332, 3.18, 2.929, 3.054,
3.579, 3.886, 3.145, 2.984, 3.305, 3.535, 3.59, 4.079, 4.141,
3.957, 3.786, 3.505, 3.101, 2.892, 2.501, 2.243, 2.151, 1.96,
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26.97, 28.061, 26.099, 25.133, 23.174, 20.483, 19.969, 21.094,
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37.34, 35.794, 35.368, 34.635, 33.84, 33.404, 32.614, 30.159,
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48.64, 51.539, 52.353, 51.426, 50.687, 48.629, 46.685, 48.598,
48.944, 48.143, 46.887, 47.518, 46.229, 45.746, 46.078, 47.155,
46.933, 49.07, 49.243, 48.567, 48.352, 48.28, 48.954, 49.512,
48.704, 49.681, 50.053, 50.957, 49.603, 47.978, 50, 50.963, 50.415,
50.266, 50.155, 50.662, 51.918, 53.166, 52.792, 54.156, 55.07,
53.702, 54.07, 54.098, 54.46, 53.783, 54.023, 53.997, 55.079,
54.819, 54.935, 53.642, 54.181, 54.903, 56.18, 56.016, 55.307,
53.558, 52.03, 54.562, 58.919, 58.351, 57.536, 59.128, 60.585,
60.938, 60.858, 60.539, 59.114, 57.326, 58.094, 58.355, 58.507,
57.85, 55.829, 57.801, 59.946, 58.755, 56.767, 55.223, 55.452,
58.029, 60.207, 60.816, 60.403, 59.365, 58.132, 57.989, 59.469,
58.991, 58.748, 58.43, 57.926, 58.671, 58.192, 58.335, 58.222,
58.802, 58.458, 58.537, 59.014, 59.015, 57.656, 56.055, 53.788,
54.659, 53.911, 54.722, 56.387, 57.327, 57.251, 57.596, 56.207,
54.747, 53.934, 55.108, 55.669, 57.554, 57.824, 56.961, 54.605,
54.705, 54.599, 54.044, 53.627), Forecasted = c(4.843, 4.843,
4.843, 4.843, 3.695, 3.695, 3.695, 3.695, 3.313, 3.313, 3.313,
3.313, 3.198, 3.198, 3.198, 3.198, 3.74, 3.74, 3.74, 3.74, 5.115,
5.115, 5.115, 5.115, 7.01, 7.01, 7.01, 7.01, 9.236, 9.236, 9.236,
9.236, 16.695, 16.695, 16.695, 16.695, 28.66, 28.66, 28.66, 28.66,
34.778, 34.778, 34.778, 34.778, 35.03, 35.03, 35.03, 35.03, 32.805,
32.805, 32.805, 32.805, 28.593, 28.593, 28.593, 28.593, 22.283,
22.283, 22.283, 22.283, 19.164, 19.164, 19.164, 19.164, 17.839,
17.839, 17.839, 17.839, 15.454, 15.454, 15.454, 15.454, 12.195,
12.195, 12.195, 12.195, 8.568, 8.568, 8.568, 8.568, 7.477, 7.477,
7.477, 7.477, 6.314, 6.314, 6.314, 6.314, 5.763, 5.763, 5.763,
5.763, 4.615, 4.615, 4.615, 4.615, 3.57, 3.57, 3.57, 3.57, 3.438,
3.438, 3.438, 3.438, 3.618, 3.618, 3.618, 3.618, 2.983, 2.983,
2.983, 2.983, 2.513, 2.513, 2.513, 2.513, 2.075, 2.075, 2.075,
2.075, 2.015, 2.015, 2.015, 2.015, 2.315, 2.315, 2.315, 2.315,
2.325, 2.325, 2.325, 2.325, 2.363, 2.363, 2.363, 2.363, 2.058,
2.058, 2.058, 2.058, 1.455, 1.455, 1.455, 1.455, 1.878, 1.878,
1.878, 1.878, 2.165, 2.165, 2.165, 2.165, 2.633, 2.633, 2.633,
2.633, 3.279, 3.279, 3.279, 3.279, 2.935, 2.935, 2.935, 2.935,
2.833, 2.833, 2.833, 2.833, 2.89, 2.89, 2.89, 2.89, 3.952, 3.952,
3.952, 3.952, 4.037, 4.037, 4.037, 4.037, 4.311, 4.311, 4.311,
4.311, 4.242, 4.242, 4.242, 4.242, 2.478, 2.478, 2.478, 2.478,
6.285, 6.285, 6.285, 6.285, 6.803, 6.803, 6.803, 6.803, 7.54,
7.54, 7.54, 7.54, 8.383, 8.383, 8.383, 8.383, 8.86, 8.86, 8.86,
8.86, 8.65, 8.65, 8.65, 8.65, 8.11, 8.11, 8.11, 8.11, 7.783,
7.783, 7.783, 7.783, 6.995, 6.995, 6.995, 6.995, 5.98, 5.98,
5.98, 5.98, 5.22, 5.22, 5.22, 5.22, 5.023, 5.023, 5.023, 5.023,
7.343, 7.343, 7.343, 7.343, 7.29, 7.29, 7.29, 7.29, 7.315, 7.315,
7.315, 7.315, 7.63, 7.63, 7.63, 7.63, 7.875, 7.875, 7.875, 7.875,
7.993, 7.993, 7.993, 7.993, 5.315, 5.315, 5.315, 5.315, 5.787,
5.787, 5.787, 5.787, 5.097, 5.097, 5.097, 5.097, 6.878, 6.878,
6.878, 6.878, 6.337, 6.337, 6.337, 6.337, 4.025, 4.025, 4.025,
4.025, 2.665, 2.665, 2.665, 2.665, 4.573, 4.573, 4.573, 4.573,
6.248, 6.248, 6.248, 6.248, 5.534, 5.534, 5.534, 5.534, 6.268,
6.268, 6.268, 6.268, 5.964, 5.964, 5.964, 5.964, 5.23, 5.23,
5.23, 5.23, 7.155, 7.155, 7.155, 7.155, 9.775, 9.775, 9.775,
9.775, 12.565, 12.565, 12.565, 12.565, 11.868, 11.868, 11.868,
11.868, 11.473, 11.473, 11.473, 11.473, 10.698, 10.698, 10.698,
10.698, 12.933, 12.933, 12.933, 12.933, 13.148, 13.148, 13.148,
13.148, 12.845, 12.845, 12.845, 12.845, 11.588, 11.588, 11.588,
11.588, 16.374, 16.374, 16.374, 16.374, 19.543, 19.543, 19.543,
19.543, 21.938, 21.938, 21.938, 21.938, 22.425, 22.425, 22.425,
22.425, 22.738, 22.738, 22.738, 22.738, 28.695, 28.695, 28.695,
28.695, 29.352, 29.352, 29.352, 29.352, 34.76, 34.76, 34.76,
34.76, 31.81, 31.81, 31.81, 31.81, 33.791, 33.791, 33.791, 33.791,
35.23, 35.23, 35.23, 35.23, 35.308, 35.308, 35.308, 35.308, 35.13,
35.13, 35.13, 35.13, 37.11, 37.11, 37.11, 37.11, 37.073, 37.073,
37.073, 37.073, 40.596, 40.596, 40.596, 40.596, 40.7, 40.7, 40.7,
40.7, 45.062, 45.062, 45.062, 45.062, 43.892, 43.892, 43.892,
43.892, 44.33, 44.33, 44.33, 44.33, 42.638, 42.638, 42.638, 42.638,
45.95, 45.95, 45.95, 45.95, 50.997, 50.997, 50.997, 50.997, 50.458,
50.458, 50.458, 50.458, 49.61, 49.61, 49.61, 49.61, 50.95, 50.95,
50.95, 50.95, 49.273, 49.273, 49.273, 49.273, 46.993, 46.993,
46.993, 46.993, 47.368, 47.368, 47.368, 47.368, 48.526, 48.526,
48.526, 48.526, 51.974, 51.974, 51.974, 51.974, 61.343, 61.343,
61.343, 61.343, 61.38, 61.38, 61.38, 61.38, 61.763, 61.763, 61.763,
61.763, 61.96, 61.96, 61.96, 61.96, 59.295, 59.295, 59.295, 59.295,
59.98, 59.98, 59.98, 59.98, 59.233, 59.233, 59.233, 59.233, 56.633,
56.633, 56.633, 56.633, 57.81, 57.81, 57.81, 57.81, 63.66, 63.66,
63.66, 63.66, 67.34, 67.34, 67.34, 67.34, 67.59, 67.59, 67.59,
67.59, 68.453, 68.453, 68.453, 68.453, 66.291, 66.291, 66.291,
66.291, 62.495, 62.495, 62.495, 62.495, 61.142, 61.142, 61.142,
61.142, 51.96, 51.96, 51.96, 51.96, 48.465, 48.465, 48.465, 48.465,
51.932, 51.932, 51.932, 51.932, 54.498, 54.498, 54.498, 54.498,
56.787, 56.787, 56.787, 56.787, 58.053, 58.053, 58.053, 58.053,
57.649, 57.649, 57.649, 57.649, 54.156, 54.156, 54.156, 54.156,
39.908, 39.908, 39.908, 39.908, 40.138, 40.138, 40.138, 40.138,
41.303, 41.303, 41.303, 41.303, 41.175, 41.175, 41.175, 41.175,
39.023, 39.023, 39.023, 39.023, 39.09, 39.09, 39.09, 39.09, 39.238,
39.238, 39.238, 39.238, 44.308, 44.308, 44.308, 44.308, 41.595,
41.595, 41.595, 41.595, 45.503, 45.503, 45.503, 45.503, 45.773,
45.773, 45.773, 45.773, 46.703, 46.703, 46.703, 46.703, 51.37,
51.37, 51.37, 51.37, 50.908, 50.908, 50.908, 50.908, 46.036,
46.036, 46.036, 46.036, 43.526, 43.526, 43.526, 43.526, 38.05,
38.05, 38.05, 38.05, 34.29, 34.29, 34.29, 34.29, 34.306, 34.306,
34.306, 34.306, 37.668, 37.668, 37.668, 37.668, 41.563, 41.563,
41.563, 41.563, 41.171, 41.171, 41.171, 41.171, 42.225, 42.225,
42.225, 42.225, 43.463, 43.463, 43.463, 43.463)), row.names = c(NA,
672L), class = "data.frame")
我想计算数据集每个（日历）周的平均绝对百分比误差，我尝试了几种方法，例如函数 apply.weekly，但其中 none 似乎工作。
apply.weekly(df, function(x) mean(abs((df$Actual-df$Forecasted)/df$Actual)))
我一直在为数据集的每个季度获取相同的值作为结果，而不是每周的结果。 你对如何实现这个计算有什么想法吗？
预先感谢您的帮助。
也许是这样：
library(lubridate)
library(magrittr)
df %>% group_by( isoweek( DATETIME ), year( DATETIME ) ) %>%
summarise( DateCount = n_distinct(date(DATETIME)), MeanError = mean( abs(Forecasted-Actual)/Actual ) )
它输出：
`isoweek(DATETIME)` `year(DATETIME)` DateCount MeanError
<dbl> <dbl> <int> <dbl>
1 44 2020 1 0.858
2 45 2020 6 0.357
使用更新后的数据源：
df <- read.delim( "https://raw.githubusercontent.com/Argiro1983/data-set/main/test1", sep=";" ) %>%
as_tibble
df %<>% mutate( DATETIME = dmy_hm(DATETIME) )
df %>% group_by( year( DATETIME ), isoweek( DATETIME ) ) %>%
summarise( DateCount = n_distinct(date(DATETIME)), MeanError = mean( abs(Forecasted-Actual)/Actual ) )
输出：
`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
如果您设置正确，日期应该可以正常工作。