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我正在尝试使用 NNETAR 对具有季节性的数据进行预测（每 12 个月）；在控制台中，它返回一条警告消息：

Warning message:
Series too short for seasonal lags

我恳请有关如何摆脱这种情况的帮助，并确保已考虑到季节性因素

下面是一个具有相同行为的简化示例：

library(lubridate)
library(tidyverse)
library(tsibble)
library(fable)
#
past <- tibble(yyyy_mm = seq(as.Date("1900-01-01"),as.Date("2020-05-01"),"month"),
              avg_d = runif(1445,1000000,2500000)) %>%
  mutate(yyyy_mm = yearmonth(yyyy_mm)) %>% 
  as_tsibble()
#
future <- tibble(yyyy_mm = seq(as.Date("2020-06-01"),as.Date("2020-12-01"),"month")) %>%
  mutate(yyyy_mm = yearmonth(yyyy_mm)) %>% 
  as_tsibble()
#
fit <- past %>% model(nnar = NNETAR(avg_d ~ AR(p=2,P=1,period=12)))
forecast(fit,future)

谢谢你的支持！

4

1 回答 1

1

由于future用于参数new_datain的数据集forecast()太短，因此返回警告。为了摆脱警告并考虑到P = 1，可以扩展未来的数据集。附有可重现的示例。

library(lubridate)
#> 
#> Attache Paket: 'lubridate'
#> The following objects are masked from 'package:base':
#> 
#>     date, intersect, setdiff, union
library(tidyverse)
library(tsibble)
#> 
#> Attache Paket: 'tsibble'
#> The following object is masked from 'package:lubridate':
#> 
#>     interval
library(fable)
#> Lade nötiges Paket: fabletools
#
past <- tibble(yyyy_mm = seq(as.Date("1900-01-01"),as.Date("2020-05-01"),"month"),
               avg_d = runif(1445,1000000,2500000)) %>%
  mutate(yyyy_mm = yearmonth(yyyy_mm)) %>% 
  as_tsibble()
#> Using `yyyy_mm` as index variable.

future <- tibble(yyyy_mm = seq(as.Date("2020-06-01"),as.Date("2020-12-01"),"month")) %>%
  mutate(yyyy_mm = yearmonth(yyyy_mm)) %>% 
  as_tsibble()
#> Using `yyyy_mm` as index variable.

fit <- past %>% model(nnar = NNETAR(avg_d ~ AR(p=2,P=1,period=12)))
forecast(fit,future)
#> Warning: Series too short for seasonal lags
#> # A fable: 7 x 4 [1M]
#> # Key:     .model [1]
#>   .model  yyyy_mm        avg_d    .mean
#>   <chr>     <mth>       <dist>    <dbl>
#> 1 nnar   2020 Jun sample[1000] 1758676.
#> 2 nnar   2020 Jul sample[1000] 1743311.
#> 3 nnar   2020 Aug sample[1000] 1766885.
#> 4 nnar   2020 Sep sample[1000] 1764609.
#> 5 nnar   2020 Okt sample[1000] 1744808.
#> 6 nnar   2020 Nov sample[1000] 1715543.
#> 7 nnar   2020 Dez sample[1000] 1738346.

# reason for the warning message: 
# inside forecast.NNETAR specials_nnetar() is called: 
P <- 1
period <- 12

# taken from specials_nnetar(): 
if (P > 0 && NROW(future) < period * P + 2) {
  rlang::warn("Series too short for seasonal lags")
  P <- 0
}
#> Warning: Series too short for seasonal lags

# extend future; NROW(future) must be equal to period * P + 2 = 14: 
future2 <- tibble(yyyy_mm = seq(as.Date("2020-06-01"),as.Date("2021-07-01"),"month")) %>%
  mutate(yyyy_mm = yearmonth(yyyy_mm)) %>% 
  as_tsibble()
#> Using `yyyy_mm` as index variable.

P <- 1
period <- 12

# taken from specials_nnetar(): 
if (P > 0 && NROW(future2) < period * P + 2) {
  rlang::warn("Series too short for seasonal lags")
  P <- 0
}
forecast(fit,future2)
#> # A fable: 14 x 4 [1M]
#> # Key:     .model [1]
#>    .model  yyyy_mm        avg_d    .mean
#>    <chr>     <mth>       <dist>    <dbl>
#>  1 nnar   2020 Jun sample[1000] 1726252.
#>  2 nnar   2020 Jul sample[1000] 1733855.
#>  3 nnar   2020 Aug sample[1000] 1750326.
#>  4 nnar   2020 Sep sample[1000] 1752966.
#>  5 nnar   2020 Okt sample[1000] 1759741.
#>  6 nnar   2020 Nov sample[1000] 1729142.
#>  7 nnar   2020 Dez sample[1000] 1746633.
#>  8 nnar   2021 Jan sample[1000] 1756297.
#>  9 nnar   2021 Feb sample[1000] 1738609.
#> 10 nnar   2021 Mrz sample[1000] 1748182.
#> 11 nnar   2021 Apr sample[1000] 1719133.
#> 12 nnar   2021 Mai sample[1000] 1718844.
#> 13 nnar   2021 Jun sample[1000] 1745875.
#> 14 nnar   2021 Jul sample[1000] 1730472.

由reprex 包于 2020-07-01 创建(v0.3.0)

于 2020-07-01T09:26:00.600 回答