使用带寓言和咕噜声的 ETS 的动态趋势或季节性参数的问题
problem using a dynamic trend or seasonal parameter with ETS with fable and purrr
我有一个 tsibble 如下所示。
test.data <- structure(list(RSLITM = c("004", "004", "004", "004", "004",
"004", "004", "004", "004", "004", "004", "004", "004", "004",
"004", "004", "004", "004", "004", "004", "004", "004", "004",
"004", "004", "004", "004", "004", "004", "004", "004", "004",
"004", "004", "004", "004", "004", "005", "005", "005", "005",
"005", "005", "005", "005", "005", "005", "005", "005", "005",
"005", "005", "005", "005", "005", "005", "005", "005", "005",
"005", "005", "005", "005", "005", "005", "005", "005", "005",
"005", "005", "005", "005", "005", "005"), RSFMTH = structure(c(17713,
17744, 17775, 17805, 17836, 17866, 17897, 17928, 17956, 17987,
18017, 18048, 18078, 18109, 18140, 18170, 18201, 18231, 18262,
18293, 18322, 18353, 18383, 18414, 18444, 18475, 18506, 18536,
18567, 18597, 18628, 18659, 18687, 18718, 18748, 18779, 18809,
17713, 17744, 17775, 17805, 17836, 17866, 17897, 17928, 17956,
17987, 18017, 18048, 18078, 18109, 18140, 18170, 18201, 18231,
18262, 18293, 18322, 18353, 18383, 18414, 18444, 18475, 18506,
18536, 18567, 18597, 18628, 18659, 18687, 18718, 18748, 18779,
18809), class = c("yearmonth", "vctrs_vctr")), RSFQTY = c(285600,
352200, 273600, 282700, 175800, 138700, 177700, 245900, 165000,
180100, 298000, 173800, 257300, 282800, 164500, 155100, 232300,
175500, 226000, 287100, 221400, 270800, 286200, 394400, 336600,
331000, 224600, 216800, 351600, 374700, 173500, 423700, 357700,
245200, 454700, 361700, 381200, 79000, 58100, 66300, 52700, 68600,
33000, 76600, 85600, 84100, 49000, 98000, 113500, 83800, 64000,
116800, 72000, 65200, 49800, 33300, 79800, 48000, 81600, 125000,
53500, 97600, 80000, 81900, 80000, 53800, 39000, 73800, 76600,
33700, 60200, 84000, 66600, 32400), RSSEAS = c("A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A"), RSTREND = c("N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N"), RSMODE = c("EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP")), row.names = c(NA,
-74L), key = structure(list(RSLITM = c("004", "005"), RSSEAS = c("A",
"A"), RSTREND = c("N", "N"), RSMODE = c("EXP", "EXP"), .rows = structure(list(
1:37, 38:74), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -2L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), index = structure("RSFMTH", ordered = TRUE), index2 = "RSFMTH", interval = structure(list(
year = 0, quarter = 0, month = 1, week = 0, day = 0, 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"))
我想使用 tsibble 中保存的参数应用修改后的 ETS 函数。例如，RSSEAS 和 RSTREND 列中的任何内容都将用于估计 ETS 模型。
以下作品：
test.data %>% model(EXP = ETS(RSFQTY ~ trend("N") + season("A")))
但是，当我尝试使用下面的函数为每个 SKU 提取参数时（因为可能每个 SKU 的参数可能不同），我收到一条错误消息。
ets.function <- function(tsib){
season.param <- as.character(tsib[1, "RSSEAS"])
trend.param <- as.character(tsib[1, "RSTREND"])
tsib %>% model(EXP = ETS(RSFQTY ~ trend(trend.param) + season(season.param))) %>% forecast(h = "3 years")
}
如果我将 ets.function(test.data) R returns 称为寓言，但它是 NULL/NA，因为未使用指定参数估计模型。
调用 map_dfr(test.data, ets.function) 出现以下错误：
Error in tsib[1, "RSSEAS"] : incorrect number of dimensions
这对我来说没有意义，因为如果我在我的控制台中 运行 season.param 或 trend.param 的代码，我会得到相应的“A”或“N” ，这正是趋势和季节特价在 ETS 函数中所取的值。
基本上，我试图找出一种方法，使用每个唯一键组合的预先指定参数，将 ETS 映射到我的 tsibble 上。我对如何实现这一点的其他想法持开放态度（pmap_dfr 参数向量等）。
我们可以用 glue 或 paste
创建公式
library(fable)
ets.function <- function(tsib){
season.param <- tsib[["RSSEAS"]][1]
trend.param <- tsib[["RSTREND"]][1]
fmla <- as.formula(glue::glue("RSFQTY ~ trend('{trend.param}') +",
" season('{season.param}')"))
print(fmla)
tsib %>%
model(EXP = ETS(fmla)) %>%
forecast(h = "3 years")
}
-测试
> ets.function(test.data)
RSFQTY ~ trend("N") + season("A")
<environment: 0x7fface3d9778>
# A fable: 72 x 8 [1M]
# Key: RSLITM, RSSEAS, RSTREND, RSMODE, .model [2]
RSLITM RSSEAS RSTREND RSMODE .model RSFMTH RSFQTY .mean
<chr> <chr> <chr> <chr> <chr> <mth> <dist> <dbl>
1 004 A N EXP EXP 2021 Aug N(4e+05, 1.4e+10) 395706.
2 004 A N EXP EXP 2021 Sep N(279181, 8.6e+09) 279181.
3 004 A N EXP EXP 2021 Oct N(266837, 8.8e+09) 266837.
4 004 A N EXP EXP 2021 Nov N(349230, 1.4e+10) 349230.
5 004 A N EXP EXP 2021 Dec N(327811, 1.4e+10) 327811.
6 004 A N EXP EXP 2022 Jan N(265657, 1.2e+10) 265657.
7 004 A N EXP EXP 2022 Feb N(375557, 1.9e+10) 375557.
8 004 A N EXP EXP 2022 Mar N(3e+05, 1.6e+10) 300908.
9 004 A N EXP EXP 2022 Apr N(318455, 1.8e+10) 318455.
10 004 A N EXP EXP 2022 May N(4e+05, 2.4e+10) 400250.
# … with 62 more rows
或者也可以使用 sprintf
ets.function <- function(tsib){
season.param <- tsib[["RSSEAS"]][1]
trend.param <- tsib[["RSTREND"]][1]
fmla <- as.formula(sprintf("RSFQTY ~ trend('%s') + season('%s')",
trend.param, season.param))
print(fmla)
tsib %>%
model(EXP = ETS(fmla)) %>%
forecast(h = "3 years")
}
ets.function(test.data)
如果我们需要基于组应用，我们可以使用 group_split 拆分并使用 map
循环应用函数
library(purrr)
test.data %>%
group_split(RSLITM) %>%
map(~ ets.function(.x))
-输出
RSFQTY ~ trend("N") + season("A")
<environment: 0x7ffac94171b8>
RSFQTY ~ trend("N") + season("A")
<environment: 0x7ffacf3f1950>
[[1]]
# A fable: 36 x 8 [1M]
# Key: RSLITM, RSSEAS, RSTREND, RSMODE, .model [1]
RSLITM RSSEAS RSTREND RSMODE .model RSFMTH RSFQTY .mean
<chr> <chr> <chr> <chr> <chr> <mth> <dist> <dbl>
1 004 A N EXP EXP 2021 Aug N(4e+05, 1.4e+10) 395706.
2 004 A N EXP EXP 2021 Sep N(279181, 8.6e+09) 279181.
3 004 A N EXP EXP 2021 Oct N(266837, 8.8e+09) 266837.
4 004 A N EXP EXP 2021 Nov N(349230, 1.4e+10) 349230.
5 004 A N EXP EXP 2021 Dec N(327811, 1.4e+10) 327811.
6 004 A N EXP EXP 2022 Jan N(265657, 1.2e+10) 265657.
7 004 A N EXP EXP 2022 Feb N(375557, 1.9e+10) 375557.
8 004 A N EXP EXP 2022 Mar N(3e+05, 1.6e+10) 300908.
9 004 A N EXP EXP 2022 Apr N(318455, 1.8e+10) 318455.
10 004 A N EXP EXP 2022 May N(4e+05, 2.4e+10) 400250.
# … with 26 more rows
[[2]]
# A fable: 36 x 8 [1M]
# Key: RSLITM, RSSEAS, RSTREND, RSMODE, .model [1]
RSLITM RSSEAS RSTREND RSMODE .model RSFMTH RSFQTY .mean
<chr> <chr> <chr> <chr> <chr> <mth> <dist> <dbl>
1 005 A N EXP EXP 2021 Aug N(67121, 4.1e+08) 67121.
2 005 A N EXP EXP 2021 Sep N(95706, 8.3e+08) 95706.
3 005 A N EXP EXP 2021 Oct N(73173, 4.9e+08) 73173.
4 005 A N EXP EXP 2021 Nov N(57981, 3.1e+08) 57981.
5 005 A N EXP EXP 2021 Dec N(42901, 1.7e+08) 42901.
6 005 A N EXP EXP 2022 Jan N(62766, 3.6e+08) 62766.
7 005 A N EXP EXP 2022 Feb N(79394, 5.7e+08) 79394.
8 005 A N EXP EXP 2022 Mar N(61960, 3.5e+08) 61960.
9 005 A N EXP EXP 2022 Apr N(60882, 3.4e+08) 60882.
10 005 A N EXP EXP 2022 May N(106257, 1e+09) 106257.
# … with 26 more rows
我有一个 tsibble 如下所示。
test.data <- structure(list(RSLITM = c("004", "004", "004", "004", "004",
"004", "004", "004", "004", "004", "004", "004", "004", "004",
"004", "004", "004", "004", "004", "004", "004", "004", "004",
"004", "004", "004", "004", "004", "004", "004", "004", "004",
"004", "004", "004", "004", "004", "005", "005", "005", "005",
"005", "005", "005", "005", "005", "005", "005", "005", "005",
"005", "005", "005", "005", "005", "005", "005", "005", "005",
"005", "005", "005", "005", "005", "005", "005", "005", "005",
"005", "005", "005", "005", "005", "005"), RSFMTH = structure(c(17713,
17744, 17775, 17805, 17836, 17866, 17897, 17928, 17956, 17987,
18017, 18048, 18078, 18109, 18140, 18170, 18201, 18231, 18262,
18293, 18322, 18353, 18383, 18414, 18444, 18475, 18506, 18536,
18567, 18597, 18628, 18659, 18687, 18718, 18748, 18779, 18809,
17713, 17744, 17775, 17805, 17836, 17866, 17897, 17928, 17956,
17987, 18017, 18048, 18078, 18109, 18140, 18170, 18201, 18231,
18262, 18293, 18322, 18353, 18383, 18414, 18444, 18475, 18506,
18536, 18567, 18597, 18628, 18659, 18687, 18718, 18748, 18779,
18809), class = c("yearmonth", "vctrs_vctr")), RSFQTY = c(285600,
352200, 273600, 282700, 175800, 138700, 177700, 245900, 165000,
180100, 298000, 173800, 257300, 282800, 164500, 155100, 232300,
175500, 226000, 287100, 221400, 270800, 286200, 394400, 336600,
331000, 224600, 216800, 351600, 374700, 173500, 423700, 357700,
245200, 454700, 361700, 381200, 79000, 58100, 66300, 52700, 68600,
33000, 76600, 85600, 84100, 49000, 98000, 113500, 83800, 64000,
116800, 72000, 65200, 49800, 33300, 79800, 48000, 81600, 125000,
53500, 97600, 80000, 81900, 80000, 53800, 39000, 73800, 76600,
33700, 60200, 84000, 66600, 32400), RSSEAS = c("A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A"), RSTREND = c("N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N",
"N", "N", "N", "N", "N"), RSMODE = c("EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP",
"EXP", "EXP", "EXP", "EXP", "EXP", "EXP", "EXP")), row.names = c(NA,
-74L), key = structure(list(RSLITM = c("004", "005"), RSSEAS = c("A",
"A"), RSTREND = c("N", "N"), RSMODE = c("EXP", "EXP"), .rows = structure(list(
1:37, 38:74), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -2L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), index = structure("RSFMTH", ordered = TRUE), index2 = "RSFMTH", interval = structure(list(
year = 0, quarter = 0, month = 1, week = 0, day = 0, 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"))
我想使用 tsibble 中保存的参数应用修改后的 ETS 函数。例如，RSSEAS 和 RSTREND 列中的任何内容都将用于估计 ETS 模型。
以下作品：
test.data %>% model(EXP = ETS(RSFQTY ~ trend("N") + season("A")))
但是，当我尝试使用下面的函数为每个 SKU 提取参数时（因为可能每个 SKU 的参数可能不同），我收到一条错误消息。
ets.function <- function(tsib){
season.param <- as.character(tsib[1, "RSSEAS"])
trend.param <- as.character(tsib[1, "RSTREND"])
tsib %>% model(EXP = ETS(RSFQTY ~ trend(trend.param) + season(season.param))) %>% forecast(h = "3 years")
}
如果我将 ets.function(test.data) R returns 称为寓言，但它是 NULL/NA，因为未使用指定参数估计模型。
调用 map_dfr(test.data, ets.function) 出现以下错误：
Error in tsib[1, "RSSEAS"] : incorrect number of dimensions
这对我来说没有意义，因为如果我在我的控制台中 运行 season.param 或 trend.param 的代码，我会得到相应的“A”或“N” ，这正是趋势和季节特价在 ETS 函数中所取的值。
基本上，我试图找出一种方法，使用每个唯一键组合的预先指定参数，将 ETS 映射到我的 tsibble 上。我对如何实现这一点的其他想法持开放态度（pmap_dfr 参数向量等）。
我们可以用 glue 或 paste
library(fable)
ets.function <- function(tsib){
season.param <- tsib[["RSSEAS"]][1]
trend.param <- tsib[["RSTREND"]][1]
fmla <- as.formula(glue::glue("RSFQTY ~ trend('{trend.param}') +",
" season('{season.param}')"))
print(fmla)
tsib %>%
model(EXP = ETS(fmla)) %>%
forecast(h = "3 years")
}
-测试
> ets.function(test.data)
RSFQTY ~ trend("N") + season("A")
<environment: 0x7fface3d9778>
# A fable: 72 x 8 [1M]
# Key: RSLITM, RSSEAS, RSTREND, RSMODE, .model [2]
RSLITM RSSEAS RSTREND RSMODE .model RSFMTH RSFQTY .mean
<chr> <chr> <chr> <chr> <chr> <mth> <dist> <dbl>
1 004 A N EXP EXP 2021 Aug N(4e+05, 1.4e+10) 395706.
2 004 A N EXP EXP 2021 Sep N(279181, 8.6e+09) 279181.
3 004 A N EXP EXP 2021 Oct N(266837, 8.8e+09) 266837.
4 004 A N EXP EXP 2021 Nov N(349230, 1.4e+10) 349230.
5 004 A N EXP EXP 2021 Dec N(327811, 1.4e+10) 327811.
6 004 A N EXP EXP 2022 Jan N(265657, 1.2e+10) 265657.
7 004 A N EXP EXP 2022 Feb N(375557, 1.9e+10) 375557.
8 004 A N EXP EXP 2022 Mar N(3e+05, 1.6e+10) 300908.
9 004 A N EXP EXP 2022 Apr N(318455, 1.8e+10) 318455.
10 004 A N EXP EXP 2022 May N(4e+05, 2.4e+10) 400250.
# … with 62 more rows
或者也可以使用 sprintf
ets.function <- function(tsib){
season.param <- tsib[["RSSEAS"]][1]
trend.param <- tsib[["RSTREND"]][1]
fmla <- as.formula(sprintf("RSFQTY ~ trend('%s') + season('%s')",
trend.param, season.param))
print(fmla)
tsib %>%
model(EXP = ETS(fmla)) %>%
forecast(h = "3 years")
}
ets.function(test.data)
如果我们需要基于组应用，我们可以使用 group_split 拆分并使用 map
library(purrr)
test.data %>%
group_split(RSLITM) %>%
map(~ ets.function(.x))
-输出
RSFQTY ~ trend("N") + season("A")
<environment: 0x7ffac94171b8>
RSFQTY ~ trend("N") + season("A")
<environment: 0x7ffacf3f1950>
[[1]]
# A fable: 36 x 8 [1M]
# Key: RSLITM, RSSEAS, RSTREND, RSMODE, .model [1]
RSLITM RSSEAS RSTREND RSMODE .model RSFMTH RSFQTY .mean
<chr> <chr> <chr> <chr> <chr> <mth> <dist> <dbl>
1 004 A N EXP EXP 2021 Aug N(4e+05, 1.4e+10) 395706.
2 004 A N EXP EXP 2021 Sep N(279181, 8.6e+09) 279181.
3 004 A N EXP EXP 2021 Oct N(266837, 8.8e+09) 266837.
4 004 A N EXP EXP 2021 Nov N(349230, 1.4e+10) 349230.
5 004 A N EXP EXP 2021 Dec N(327811, 1.4e+10) 327811.
6 004 A N EXP EXP 2022 Jan N(265657, 1.2e+10) 265657.
7 004 A N EXP EXP 2022 Feb N(375557, 1.9e+10) 375557.
8 004 A N EXP EXP 2022 Mar N(3e+05, 1.6e+10) 300908.
9 004 A N EXP EXP 2022 Apr N(318455, 1.8e+10) 318455.
10 004 A N EXP EXP 2022 May N(4e+05, 2.4e+10) 400250.
# … with 26 more rows
[[2]]
# A fable: 36 x 8 [1M]
# Key: RSLITM, RSSEAS, RSTREND, RSMODE, .model [1]
RSLITM RSSEAS RSTREND RSMODE .model RSFMTH RSFQTY .mean
<chr> <chr> <chr> <chr> <chr> <mth> <dist> <dbl>
1 005 A N EXP EXP 2021 Aug N(67121, 4.1e+08) 67121.
2 005 A N EXP EXP 2021 Sep N(95706, 8.3e+08) 95706.
3 005 A N EXP EXP 2021 Oct N(73173, 4.9e+08) 73173.
4 005 A N EXP EXP 2021 Nov N(57981, 3.1e+08) 57981.
5 005 A N EXP EXP 2021 Dec N(42901, 1.7e+08) 42901.
6 005 A N EXP EXP 2022 Jan N(62766, 3.6e+08) 62766.
7 005 A N EXP EXP 2022 Feb N(79394, 5.7e+08) 79394.
8 005 A N EXP EXP 2022 Mar N(61960, 3.5e+08) 61960.
9 005 A N EXP EXP 2022 Apr N(60882, 3.4e+08) 60882.
10 005 A N EXP EXP 2022 May N(106257, 1e+09) 106257.
# … with 26 more rows