R中的簡單移動平均列方式

Question

所以我每個季度都會清理收入數據，我需要做兩個季度的移動平均數來預測每個單獨產品未來五年的季度收入（我知道現在最終會是相同的平均值）。 這里附上數據框： Revenue Df

現在我有寬格式的數據，你會看到我通過讓用戶輸入預測的開始和結束日期來創建空的預測列，然后它為每個季度之間的每個季度創建列。 如何使用移動平均線填充這些預測？ 我也把它轉換成long了，還是不知道怎么填預測。 我也知道 9-30-2020 顯示在預測中，即使用戶輸入了預測日期，我們也希望將其替換為實際值。

for(i in ncol(Revenue_df)){
  
  if(i<3)
  {Revenue_df[,i]<- Revenue_df[,i]}
  else{
  Revenue_df[,i]<-(Revenue_df[,i-1]+Revenue_df[,i-2])/2
  }
  
}

Product<- c("a","b","c","d","e")
Revenue.3_30_2020<- c(50,40,30,20,10)
Revenue.6_30_2020<- c(50,45,28,19,17)
Revenue.9_30_2020<- c(25,20,22,17,24)


revenue<- data.frame(Product,Revenue.3_30_2020,Revenue.6_30_2020,Revenue.9_30_2020)

forecast.sequence<- c("2020-09-30","2020-12-31","2021-03-31","2021-06-30","2021-09-30","2021-12-31","2022-03-31"
 "2022-06-30","2022-09-30","2022-12-31","2023-03-31","2023-06-30","2023-09-30","2023-12-31","2024-03-31"
"2024-06-30","2024-09-30","2024-12-31")


forecast.sequence.amount<- paste("FC.Amount.",forecast.sequence)
revenue[,forecast.sequence.amount]<-NA

我試過這段代碼，但沒有用，有什么建議嗎？ 還附上了圖片中顯示的示例數據框的代碼，抱歉格式不正確，這是我第二次在這里提問。

Answer 1

這對於產品預測來說似乎有點簡單。 您可能需要查看forecast和fable包，以了解可以說明預測趨勢和季節性的預測函數。 然而，這些將需要兩個以上的數據點。 無論如何，將您的問題視為給定，以下代碼似乎可以滿足您的描述。

編輯

我已經將預測計算變成了一個函數，以使其更易於使用。

library(tidyverse)

product<- c("a","b","c","d","e")
Revenue.3_30_2020<- c(50,40,30,20,10)
Revenue.6_30_2020<- c(50,45,28,19,17)
Revenue.9_30_2020<- c(25,20,22,17,24)
revenue<- data.frame( Product = product, Revenue.3_30_2020,Revenue.6_30_2020,Revenue.9_30_2020)

rev_frcst <- function(revenue, frcst_end, frcst_prefix) {
#      
#  Arguments:
#     revenue = data frame with 
#               Product containing product name
#               columns with the format "prefix.m_day_year" containing product quantities for past quarters
#     frcst_end = end date for quarterly forecast
#     frcst_prefix = string containing prefix for forecast
#     
#  convert revenue to long format
#     
 rev_long <-  revenue %>% pivot_longer(cols = -Product, names_to = "Quarter", values_to = "Revenue") %>%
            mutate(quarter_end = as.Date(str_remove(Quarter,"Revenue."), "%m_%d_%Y")) 
 num_revenue <- nrow(rev_long)/length(product)      
#
#  generate forecast dates
#
 forecast.sequence <- seq( max(rev_long$quarter_end), 
                         as.Date(frcst_end),
                         by = "quarter")[-1] 
#
#  Add forecast rows to data
#
  rev_long <- rev_long %>% 
              bind_rows(expand_grid(Product=unique(revenue$Product), quarter_end = forecast.sequence) %>%
                        mutate(Quarter = paste(frcst_prefix, quarter_end)) ) )
#
#  Define moving average function
#
 mov_avg <- function(num_frcst, x) {
   y <- c(x, numeric(num_frcst))
   for(i in 1:num_frcst + 2) { 
      y[i] <- .5*(y[i-1] + y[i-2]) }
  y[1:num_frcst + 2]
 }
#
# Calculate forecast
# 
  rev_long_2 <-  rev_long %>% group_by(Product) %>% 
                 mutate(forecast = c(Revenue[1:num_revenue],
                                 mov_avg(num_frcst =length(forecast.sequence),
                                         x = Revenue[1:2 + num_revenue - 2]))) %>%
             arrange(Product, quarter_end)
}
#
#  call rev_frcst to calcuate forecast
#
  rev_forecast <- rev_frcst(revenue=revenue,
                        frcst_end = "2024-12-31",
                        frcst_prefix = "FC.Amount.")
 

這使

   Product   Quarter               Revenue quarter_end forecast
   <chr>     <chr>                   <dbl> <date>         <dbl>
   1 a       Revenue.3_30_2020          50 2020-03-30      50  
   2 a       Revenue.6_30_2020          50 2020-06-30      50  
   3 a       Revenue.9_30_2020          25 2020-09-30      25  
   4 a       FC.Amount. 2020-12-30      NA 2020-12-30      37.5
   5 a       FC.Amount. 2021-03-30      NA 2021-03-30      31.2
   6 a       FC.Amount. 2021-06-30      NA 2021-06-30      34.4
   7 a       FC.Amount. 2021-09-30      NA 2021-09-30      32.8
   8 a       FC.Amount. 2021-12-30      NA 2021-12-30      33.6
   9 a       FC.Amount. 2022-03-30      NA 2022-03-30      33.2
  10 a       FC.Amount. 2022-06-30      NA 2022-06-30      33.4