Hi Akshay,
The forecast package will do the BoxCox transform and automatically
backtransform the forecasts.
The package also handles xts objects.
For example, modifying the example from the help page of
forecast::forecast for Arima
> dt <- as.Date("2023-01-01") + 1:length(WWWusage)
> a <-
Colleagues,
Your suggestions are elegant and greatly appreciated.
Thomas Subia
On Friday, August 11, 2023 at 11:08:42 PM PDT, Berwin A Turlach
wrote:
G'day Thomas,
On Sat, 12 Aug 2023 04:17:42 + (UTC)
Thomas Subia via R-help wrote:
> Here is my reproducible code for a graph
dear members,
I have a heteroscedastic time series which I want to
transform to make it homoscedastic by a box cox transformation. I am using
Otexts by RJ hyndman and George Athanopolous as my textbook. They discuss
transformation and also say the fpp3 and the fable
Hola, Manuel:
La propuesta es lógica y funciona:
> V1 <- c (47, 71, 41, 23, 83, 152, 82, 8, 160, 18)
+ V2a <- c(NA, 36, 15, 5, 56, 18, NA, 5, NA, 5)
+ V2b <- c(37, NA, 15, NA, NA, NA, 90, NA, 161, NA)
+ # Definir una función que encapsula la lógica de la expresión
+ myFunc <- function
+ geom_ribbon(stat = "smooth",
se = TRUE,
alpha = 0, # or, use fill = NA
colour = "black",
linetype = "dotted")
Does that work?
On Sat, 12 Aug 2023, 06:12 Rui Barradas, wrote:
> Às 05:17 de 12/08/2023, Thomas Subia via R-help escreveu:
G'day Thomas,
On Sat, 12 Aug 2023 04:17:42 + (UTC)
Thomas Subia via R-help wrote:
> Here is my reproducible code for a graph using geom_smooth
The call "library(tidyverse)" was missing. :)
> I'd like to add a black boundary around the shaded area. I suspect
> this can be done with
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