Middle out fabletools 协调方法给出预测问题 function

Question

I have a grouped time series with items and their category and I would like to make 6months sales forecasting. I would like to o use intermediate level (category) to make base forecasting because the stagionality and trends maybe are better valued. So i grouped my data for key, and i would like to use middle_out approch, the total sales use bottom up and single item are forected useing top down approach I'm using fabletools middle_out function, but when i try to make forecast it doesn't work this is my code:

library(reshape)
library(tidyverse)
library(tsibble)
library(dplyr)
library(fable)
library(fpp2)
library(forecast)

#read data from csv
#example dataset
set.seed(42)  ## for sake of reproducibility
n <- 6
data_example <- data.frame(Date=seq.Date(as.Date("2020-12-01"), as.Date("2021-05-01"), "month"),
                  No_=sample(1800:1830, n, replace=TRUE),
                  Category=rep(LETTERS[1:3], n),
                  Quantity=sample(18:24, n, replace=TRUE))


sell_full <- data_example %>% mutate(Month=yearmonth(Date)) %>% group_by(No_,Category, Month) %>% summarise(Quant = sum(Quantity), .groups = 'keep')
sell_full <- na.omit(sell_full)

#data


#conversion to tsibble for forecastings

sell_full <- as_tsibble(sell_full, key=c(No_, Category), index=Month)
sell_full <- sell_full %>% aggregate_key((Category/No_), Quant= sum(Quant)) 
#sell_full<- filter(sell_full, !is.na(sell_full$Quant))

sell_full <- sell_full %>% fill_gaps(Quant=0, .full=TRUE)

fit <- sell_full %>%model(ets = ETS(Quant~ error("A") + trend("A") + season("A")))%>% middle_out(split=1)

fc <- forecast(fit, h = "6 months", level=1,lambda="auto")

if I put method="mo" in forecast method as documentation says it return this error

Error in meanf(object, h = h, level = level, fan = fan, lambda = lambda,  : 
  unused argument (method = "mo")

if i doesn't put method info in forecast it return this error:

<error/vctrs_error_ptype2>
Error in `vec_compare()`:
! Can't combine `..1` <agg_vec> and `..2` <double>.
---
Backtrace:
  1. generics::forecast(fit, h = "6 months", level = 1, lambda = "auto")
  2. forecast:::forecast.default(fit, h = "6 months", level = 1, lambda = "auto")
  3. forecast:::forecast.ts(object, ...)
  4. forecast::meanf(...)
  5. forecast::BoxCox(x, lambda)
  6. forecast::BoxCox.lambda(x, lower = -0.9)
  7. fabletools:::Ops.lst_mdl(x, 0)
 11. fabletools:::map2(e1, e2, .Generic)
 12. base::mapply(.f, .x, .y, MoreArgs = list(...), SIMPLIFY = FALSE)
 13. vctrs:::`<=.vctrs_vctr`(dots[[1L]][[1L]], dots[[2L]][[1L]])
 14. vctrs::vec_compare(e1, e2)

The Documentions about it is very bad, someone can help me?

UPDATE: As someone suggest to me, I tried to remove some package, now my library are:

library(tsibble)
library(dplyr)
library(fable)
library(fpp3)
library(conflicted)

Now the error is changed. when I try to make forecast function I have this error:

Error in build_key_data_smat(key_data) : 
  argument "key_data" is missing, with no default

and if I put key_data = "Category" (Category is the split layer) the error is:

fc <- forecast(fit, h = "6 months",level=1,lambda="auto", key_data= "Category")
Error in -ncol(x) : invalid argument to unary operator

Answer 1

library(conflicted)
library(fpp3)
library(tidyverse)
n <- 6
data_example <- data.frame(Date = seq.Date(as.Date("2020-12-01"), as.Date("2021-05-01"), "month"),
                           No_ = sample(1800:1830, n, replace = TRUE),
                           Category = rep(LETTERS[1:3], n),
                           Quantity = sample(18:24, n, replace = TRUE))

sell_full <- data_example |> mutate(Month = yearmonth(Date)) |> group_by(No_,Category, Month) |> summarise(Quant = sum(Quantity), .groups = 'keep')
sell_full <- ungroup(sell_full)
sell_full <- as_tsibble(sell_full, key = c(No_, Category), index = Month)
sell_full <- sell_full %>% aggregate_key((Category/No_), Quant = sum(Quant))
sell_full <- sell_full %>% fill_gaps(Quant = 0, .full = TRUE)
fit <- sell_full %>% model(ets = ETS(Quant~ error("A") + trend("A")))

fc <- fabletools::forecast(fit, h = "6 months", lambda = "auto")

Thought I'd have a look at the code to generate sell_full.

Added an ungroup, took out the seasonal, and took out the middle_out . Runs now, and no longer asks for key_value . The ungroup , as it seemed that you were finished with the grouping. The seasonal as it was not supported by the data. The middle out as it would cause the prompt for key_value . Spent a bit of time on the middle_out leading to forecast asking for key_value , though, hence comment above.

This led me to try another way to do middle_out:

fit <- sell_full %>% model(ets = ETS(Quant~ error("A") + trend("A"))) |> reconcile(mo = middle_out(ets))

This runs fine. This idea came from fpp3 Hoping that this helps: :-)