R auto.arima 预报

Question

我想为某事创建预测，我选择 auto.arima。训练后，我无法计算预测还有2篇文章：

my_forecast <- ts(frc$sales_30, frequency = 12)

my_forecast  <- tsclean(my_forecast)

fit <- auto.arima(my_forecast)

但我有 100 篇文章 + 我需要预测所有这些名称（格式：年、月、销售额、文章）

Answer 1

此任务在 R 中的典型工作流程是列表式的。这意味着您通过 list-items 中的文章传播数据并在这些文章上应用函数。正如您可能已经理解的那样，年份和月份是无关紧要的，因为 time-series 是由 ts() 函数的频率变量生成的。

因此，此示例仅适用于文章 A 和 B 以及它们虚构的月销售额向量，我们假设它已经按日期排序。

我不会深入研究 time-series analysis/predictions 的技术细节，而是主要关注 process/code 以基于包含所有文章（或任何级别）的 df 进行多个预测分组）和相应的销售历史记录。我没有使用 tsclean() 函数，但从工作流中应该可以看出如何包含它：

library(forecast)
library(tidyverse)
# set up some dummy data (has no clear pattern in terms of seasonality etc. but works for demo)
## bear in  mind that this is randomly generated data therefore you most likely will not reproduce my data but with the help of a seed you can work arround this as well.
df <- data.frame(article = c(rep("A", 24), rep("B", 24)), 
                 sales = c(sample(seq(from = 20, to = 50, by = 5), size = 24, replace = TRUE),
                           sample(seq(from = 20, to = 50, by = 5), size = 24, replace = TRUE)))
# build grouping inside de df/tibble
dfg <- df %>% 
    dplyr::group_by(article) 
# split the new df by grouping criteria into list
dfl <- dfg %>%
    dplyr::group_split(.keep = FALSE)
# set list names acording to article value (no needed but might be helpfull for you)
names(dfl) <- dplyr::group_keys(dfg)$article
# apply ts function with frequency 12 to the list items
dflt <- lapply(dfl, ts, frequency = 12)
# apply the auto.arima to build list of models
dfltm <- lapply(dflt, forecast::auto.arima)
# apply forecast with horizon 2 on the list of final models from auto.arima
predictions <- lapply(dfltm, forecast::forecast, h = 2)
# print results
predictions 

$A
      Point Forecast    Lo 80    Hi 80    Lo 95   Hi 95
Jan 3       34.79167 22.47636 47.10697 15.95703 53.6263
Feb 3       34.79167 22.47636 47.10697 15.95703 53.6263

$B
      Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
Jan 3       34.58333 20.32802 48.83865 12.78171 56.38496
Feb 3       34.58333 20.32802 48.83865 12.78171 56.38496

做同样事情的现代方法是在 tibble:

中使用嵌套列表

       # build list inside the tibble/df by existing groupings
npd <- tidyr::nest(dfg) %>%
                           # generate new column of ts series data
    dplyr::mutate(tsdata = purrr::map(data, ~ ts(.x, frequency = 12)),
                           # use auto.arima on the data to build new column of final auto.arima models
                  models = purrr::map(tsdata, ~ forecast::auto.arima(.x)),
                                # generate forecast as new column
                  predictions = purrr::map(models, ~ forecast::forecast(.x, h = 2))) 
# print prediction results
npd$predictions
[[1]]
      Point Forecast    Lo 80    Hi 80    Lo 95   Hi 95
Jan 3       34.79167 22.47636 47.10697 15.95703 53.6263
Feb 3       34.79167 22.47636 47.10697 15.95703 53.6263

[[2]]
      Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
Jan 3       34.58333 20.32802 48.83865 12.78171 56.38496
Feb 3       34.58333 20.32802 48.83865 12.78171 56.38496

正如最初提到的，ts() 函数基于频率而不是日期列工作，这意味着您必须确保列出没有销售的月份，并且所有文章都有完整的数据时间线，顺序越来越长（时间导向）。在形成 time-series 对象之前必须包含缺失值。

最后强烈推荐forecast包作者的开书，可以在这里找到：https://otexts.com/fpp2/

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