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(即行)转换为时间序列对象，然后在每行上应用auto.arima并进行预测。
我尝试使用apply fn:
apply(customerdata,1,as.ts)
但这不能正常工作。
还有一种方法，我可以使用tidyverse包(如purrr等)将每一行转换为ts对象，然后使用map fn应用auto.arima，然后提取错误统计信息(如MAPE)并在data.frame中预测点。
帮助将不胜感激!
最佳答案
这是使用tidyverse中的列表列的方法
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
关于r - 如何将数据帧行迭代转换为时间序列对象并将auto.arima应用于每行，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/47968665/