如何迭代地将数据帧行转换为时间序列对象，并将auto.arima应用于每一行

Question

我有以下df：

head(customerdata)
  Customer.ID Week1 Week2 Week3 week4 week5 week6 week7 week8
1          C1   420   423   481   421   393   419   415   440
2          C2  1325  1262  1376  1370  1484  1421  1287  1400
3          C3   547   541   547   550   570   576   556   587
4          C4   349   349   375   346   374   379   433   376
5          C5   721   714   758   716   833   735   711   731
6          C6   420   423   481   421   393   419   415   440

我需要将每个客户ID ierow转换为时间序列对象，然后在每一行上应用auto.arima并进行预测。

我尝试使用apply fn：

apply(customerdata,1,as.ts) 

但这并没有正常工作。

还有一种方法，我可以使用像purrr等tidyverse包将每一行转换为ts对象，然后使用map fn应用auto.arima，然后提取像MAPE这样的错误统计数据，并在data.frame中指向预测。

帮助将不胜感激!!

Answer 1

以下是如何使用tidyverse列表列来完成它

library(dplyr)
library(tidyr)
library(purrr)
library(zoo)
library(forecast)

start_date <-ymd(20171225)
holdout <- 3

customerdata %>% gather(key, value, -Customer.ID) %>% 
  mutate(key=as.numeric(str_replace(key, "[W|w]eek", ""))) %>%
  mutate(Date=start_date + weeks(key)) %>% 
  select(Customer.ID, Date, Value=value) %>% 
  group_by(Customer.ID) %>% nest() %>% 
  mutate(zoo_obj=map(data, ~with(.x, zoo(Value, Date))),
         arima_oof_mod=map(zoo_obj, ~auto.arima(head(.x, length(.x)-holdout))),
         arima_fcst=map(arima_oof_mod, forecast, holdout),
         holdout=map(zoo_obj, tail, holdout),
         metrics=map2(arima_fcst, holdout, ~accuracy(.x,.y)),
         metrics=map(metrics, ~{as.data.frame(.x) %>% tibble::rownames_to_column()})) %>%
  unnest(metrics)

#> # A tibble: 12 x 9
#>    Customer.ID      rowname            ME      RMSE      MAE         MPE     MAPE       MASE        ACF1
#>         <fctr>        <chr>         <dbl>     <dbl>    <dbl>       <dbl>    <dbl>      <dbl>       <dbl>
#>  1          C1 Training set  9.095016e-14 28.883213 21.36000 -0.43337807 4.874127 0.04995323 -0.08025508
#>  2          C1     Test set -2.933333e+00 11.350184 11.20000 -0.75682291 2.635611 0.02619270          NA
#>  3          C2 Training set -9.095086e-14 72.805494 55.92000 -0.28176887 4.091423 0.04101511  0.13187992
#>  4          C2     Test set  5.933333e+00 59.144681 56.86667  0.24382775 4.201352 0.04170945          NA
#>  5          C3 Training set  1.136868e-13  9.939819  7.60000 -0.03188731 1.365221 0.01379310  0.13157895
#>  6          C3     Test set  2.200000e+01 25.468935 22.00000  3.79081247 3.790812 0.03992740          NA
#>  7          C4 Training set  3.410570e-14 13.032268 12.72000 -0.13041415 3.526806 0.03547128 -0.54870466
#>  8          C4     Test set  3.740000e+01 45.659172 37.40000  9.06423112 9.064231 0.10429448          NA
#>  9          C5 Training set -9.095086e-14 45.239805 37.68000 -0.34415821 4.913179 0.05034741 -0.23841614
#> 10          C5     Test set -2.273333e+01 25.040501 22.73333 -3.15454237 3.154542 0.03037591          NA
#> 11          C6 Training set  9.095016e-14 28.883213 21.36000 -0.43337807 4.874127 0.04995323 -0.08025508
#> 12          C6     Test set -2.933333e+00 11.350184 11.20000 -0.75682291 2.635611 0.02619270          NA

Answer 2

没有必要将每一行转换为ts对象。 您可以在每一行上运行auto.arima ，但请务必排除第一列。

library(forecast)
arima_models <- apply(customerdata[, -1], 1, auto.arima)

然后，您可以运行以下代码以获得每个模型的一步预测

model_forecasts <- lapply(arima_models, function(x) forecast(x, h = 1))

要提取点预测，您可以使用purrr::map_*

library(purrr)
map_dbl(model_forecasts, "mean")
#       1        2        3        4        5        6 
# 426.500 1365.625  587.000  372.625  739.875  426.500

或者，如果您在forecast设置h > 1，则使用

map_dfr(model_forecasts, "mean")

要计算MAPE，您当然需要真正的结果。

数据

customerdata <- read.table(text = "Customer.ID Week1 Week2 Week3 week4 week5 week6 week7 week8
1          C1   420   423   481   421   393   419   415   440
2          C2  1325  1262  1376  1370  1484  1421  1287  1400
3          C3   547   541   547   550   570   576   556   587
4          C4   349   349   375   346   374   379   433   376
5          C5   721   714   758   716   833   735   711   731
6          C6   420   423   481   421   393   419   415   440", header = TRUE)