R - 透视预测结果

Question

我有以下源数据：

根据过去的 156 周，我想对接下来的 52 周进行预测。以下代码工作正常

my.ds <- myDS[1, -c(3,4,5,6)] #reading my source file
my.start <- myDS[1, c(3)]
my.product <- myDS[1, c("Product")]
my.product <- myDS[1, c("Location")]
my.result <- melt(my.ds, id = c("Product","Location"))
my.result[order(my.result$variable),]
my.ts <- ts(my.result$value, frequency=52, start=c(my.start,1))
my.fc <- forecast(my.ts, h=52)
my.fc

预测给出以下输出：

         Point Forecast        Lo 80     Hi 80       Lo 95     Hi 95
2003.000      1637.7675    -8.610502  3284.146  -880.15039  4155.685
2003.019      1453.9059  -195.169681  3102.981 -1068.13753  3975.949
2003.038      8668.6921  7016.923492 10320.461  6142.53000 11194.854
2003.058      5851.0741  4196.616771  7505.531  3320.79997  8381.348
2003.077      4333.9240  2676.782333  5991.066  1799.54453  6868.303
2003.096      4284.5899  2624.768291  5944.412  1746.11178  6823.068

我现在想做的是：

将产品和位置重新添加到此结果集中
添加计算列：(Hi 95) - (Point Forecast)（我也需要这个 Point Forecast 列）
将 table 转回 table 如下

在这里尝试过重塑，但不太确定如何执行它，因为结果似乎不是 table 格式。

Link to download source file in csv

Answer 1

以下确实给出了我想要的输出：

library(forecast)
library(reshape)
library(plyr)

#exclude non required columns
my.ds <- myDS[, -c(3,4,5,6)]
#set the required date, Product, Location
my.start <- myDS[1, c(3)]
my.product <- myDS[1, c("Product")]
my.location <- myDS[1, c("Location")]
#unpivot the table
my.result <- melt(my.ds, id = c("Product","Location"))
#run forecasting
# set the CIs we want to use here, so we can reuse this vector
cis <- c(80, 95)
# generate the forecast using those ci levels
my.ts <- ts(my.result$value, frequency=52, start=c(my.start,1))
f <- forecast(my.ts, h=52, level=cis)
# make a data frame containing the forecast information, including the index
z <- as.data.frame(cbind(seq(1:52),
                     f$mean,
                     Reduce(cbind, lapply(seq_along(cis), function(i)   cbind(f$lower[,i], f$upper[,i])))))
# give the columns better names
names(z) <- c("index", "mean", paste(rep(c("lower", "upper"), times =   length(cis)), rep(cis, each = 2), sep = "."))
# manipulate the results as you describe
zw <- z %>%
  # keep only the variable you want and its index
  mutate(sssf = upper.95 - mean) %>%
  select(index, mean, sssf) %>%
  # add product and location info
  mutate(product = my.product,
     location = my.location) %>%
# rearrange columns so it's easier to read
select(product, location, index, mean, sssf)
zw <- melt(zw, id.vars = c("product", "location", "index"), measure.vars = c("mean","sssf"))
data.set <- cast(zw, product + location ~ index + variable, value = "value")

R - 透视预测结果

R - Pivot the Forecast Result

r

reshape

forecasting