如何使用分组数据的动态回归模型预测 arima？答案

【问题标题】：How to forecast an arima with Dynamic regression models for grouped data?如何使用分组数据的动态回归模型预测 arima？
【发布时间】：2020-11-12 02:38:16
【问题描述】：

我正在尝试使用分组数据同时对多个 ts 进行回归（带有 ARIMA 错误的回归）的 arima 预测。

我是整理数据的新手，所以...基本上，我正在使用多变量 ts 和多变量模型重现此示例 (https://cran.rstudio.com/web/packages/sweep/vignettes/SW01_Forecasting_Time_Series_Groups.html)。

这是一个可重现的例子：

library(tidyverse); library(tidyquant)
library(timetk); library(sweep)
library(forecast)
library(tsibble)
library(fpp3)

# using package data
bike_sales

# grouping data 
monthly_qty_by_cat2 <- bike_sales %>%
  mutate(order.month = as_date(as.yearmon(order.date))) %>%
  group_by(category.secondary, order.month) %>%
  summarise(total.qty = sum(quantity), price.m = mean(price))

# using nest 
monthly_qty_by_cat2_nest <- monthly_qty_by_cat2 %>%
  group_by(category.secondary) %>%
  nest()
monthly_qty_by_cat2_nest

# Forecasting Workflow
# Step 1: Coerce to a ts object class
monthly_qty_by_cat2_ts <- monthly_qty_by_cat2_nest %>%
  mutate(data.ts = map(.x       = data, 
                       .f       = tk_ts, 
                       select   = -order.month,  # take off date 
                       start    = 2011, 
                       freq     = 12))


# Step 2: modeling an ARIMA(y ~ x)
# make a function to map
modeloARIMA_reg <- function(y,x) {
  result <- ARIMA(y ~ x)
  return(list(result))}

# map the function 
monthly_qty_by_cat2_fit <- monthly_qty_by_cat2_ts %>%
  mutate(fit.arima = map(data.ts, modeloARIMA_reg))
monthly_qty_by_cat2_fit

这里我不知道地图是否在 y 中使用了正确的变量（依赖），但我继续尝试预测并出现错误

# Step 3: Forecasting the model
monthly_qty_by_cat2_fcast <- monthly_qty_by_cat2_fit %>%
  mutate(fcast.ets = map(fit.arima, forecast))

# this give me this error
# Erro: Problem with `mutate()` input `fcast.arima`.
# x argumento não-numérico para operador binário
# i Input `fcast.arima` is `map(fit.arima, forecast)`.
# i The error occured in group 1: category.secondary = "Cross Country Race".
# Run `rlang::last_error()` to see where the error occurred.
# Além disso: Warning message:
#   In mean.default(x, na.rm = TRUE) :
#   argument is not numeric or logical: returning NA

出现两个问题：

我不知道如何输入每组自变量（x）的均值；

以及如何将这些新数据声明为预测参数。

PS：不需要tibble或嵌套结果，我只需要点预测和CI（total.qty lo.95 hi.95）

【问题讨论】：

标签： r time-series forecasting arima

【解决方案1】：

好吧，这段代码为我解决了这个问题。这对每个时间序列（分组 tsibble）进行一个预测，并使用这些时间序列自己的平均值作为预测中的未来数据欢迎任何意见。

# MY FLOW
monthly_qty_by_cat2 <- 
  sweep::bike_sales %>%
  mutate(order.month = yearmonth(order.date)) %>%
  group_by(category.secondary, order.month) %>%
  summarise(total.qty = sum(quantity), price.m = mean(price)) %>% 
  as_tsibble(index=order.month, key=category.secondary) # coerse in tsibble
# mean for the future
futuro <- monthly_qty_by_cat2 %>% 
  group_by(category.secondary) %>% 
  mutate(fut_x = mean(price.m)) %>% 
  do(price.m = head(.$fut_x,1))
# as.numeric
futuro$price.m <- as.numeric(futuro$price.m)
futuro
# make values in the future
future_x <- new_data(monthly_qty_by_cat2, 3) %>%
  left_join(futuro, by = "category.secondary")
future_x

# model and forecast
fc <- monthly_qty_by_cat2 %>% 
  group_by(category.secondary) %>% 
  model(ARIMA(total.qty ~ price.m))  %>%
  forecast(new_data=future_x)  %>% 
  hilo(level = 95) %>% 
  unpack_hilo("95%")
fc

# Tidy the forecast
fc_tibble <- fc %>%  as_tibble() %>% select(-total.qty)
fc_tibble
# the end

这为我解决了问题。这对每个组时间序列进行一个预测，并将这些组时间序列的自己的平均值用作预测中的未来数据欢迎任何意见。

【讨论】：