A fully reproducible example.
library(forecast)
date = seq(as.Date("2019/01/01"), by = "month", length.out = 48)
productB = rep("B",48)
productB = rep("B",48)
productA = rep("A",48)
productA = rep("A",48)
subproducts1=rep("1",48)
subproducts2=rep("2",48)
subproductsx=rep("x",48)
subproductsy=rep("y",48)
b1 <- c(rnorm(30,5), rep(0,18))
b2 <- c(rnorm(30,5), rep(0,18))
b3 <-c(rnorm(30,5), rep(0,18))
b4 <- c(rnorm(30,5), rep(0,18))
Created the dataframe below
dfone <- data.frame("date"= rep(date,4),
"product"= c(rep(productB,2),rep(productA,2)),
"subproduct"=
c(subproducts1,subproducts2,subproductsx,subproductsy),
"actuals"= c(b1,b2,b3,b4))
export_df <- split(dfone[1:4], dfone[3])
dummy_list <- split(dfone[1:4], dfone[3]) %>% lapply( function(x)
x[(names(x) %in% c("date", "actuals"))])
dummy_list <- lapply(dummy_list, function(x) { x["date"] <- NULL; x })
list_dfs <- list()
for (i in 1:length(unique(dfone$subproduct))) {
#assign(paste0("df", i), as.data.frame(dummy_list[[i]]))
list_dfs <-append(list_dfs,dummy_list[[i]])
}
combined_dfs <- Reduce(function(x, y) merge(x, y, all = TRUE,
by='date'), list(list_dfs))
Creating the time series
list_ts <- lapply(list_dfs, function(t)
ts(t,start=c(2019,1),end=c(2021,6), frequency = 12)) %>%
lapply( function(t) ts_split(t,sample.out=(0.2*length(t)))) #
creates my train test split
list_ts <- do.call("rbind", list_ts) #Creates a list of time series
How can I automatically create it so m1 to m6 is created automatically in the global environment? Notice how the first argument order = is the same and the 2nd argument it varies. After we use up all of the values of the 2nd order, we move on to the next element in the first argument's order.
m1<- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(1,1,1),seasonal=list(order=c(0,1,0),
period=12)) ,h=24))
m1<- lapply(m1, "[", c("mean"))
m2<- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(1,1,1),seasonal=list(order=c(1,0,0),
period=12)) ,h=24))
m2<- lapply(m2"[", c("mean"))
m3<- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(1,1,1),seasonal=list(order=c(0,0,0),
period=12)) ,h=24))
m3<- lapply(m3"[", c("mean"))
m4<- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(0,0,0),seasonal=list(order=c(0,1,0),
period=12)) ,h=24))
m4<- lapply(m4, "[", c("mean"))
m5<- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(0,0,0),seasonal=list(order=c(1,0,0),
period=12)) ,h=24))
m5<- lapply(m5"[", c("mean"))
m6<- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(0,0,0),seasonal=list(order=c(0,0,0),
period=12)) ,h=24))
m6<- lapply(m6"[", c("mean"))
I'd want to do something with this
n1 <- ((0,0,0),(1,1,1))
where each element of n1 is (0,0,0)... etc
n2 <- ((0,1,0),(1,0,1),(0,0,0))
out<- lapply(seq_along(n1), function(i) {
m<- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(0,0,0),seasonal=list(order=c(1,0,0),
period=12)),h=24)
m1<-
lapply(m1, "[", "mean")
assign(paste0("m1", i),
m1, envir = .GlobalEnv)
m1})
We create two list with vectors as elements ('n1', 'n2'). Do the expand.grid to create a two column data.frame with combination of those lists
n1 <- list(c(0,0,0), c(1,1,1))
n2 <- list(c(1,0,0),c(0, 1, 0),c(0,0,0))
dat_n <- expand.grid(n1 = n1, n2 = n2)
Loo over the sequence of rows of 'dat_n', extract the list corresponding to those columns [[i]] and specify in the order
out <- lapply(seq_len(nrow(dat_n)), function(i) {
m <- lapply(list_ts[1:(length(list_ts)/2)], function(x) {
tryCatch({forecast::forecast(arima(x, order = dat_n$n1[[i]],
seasonal=list(order = dat_n$n2[[i]],
period=12)),h=24)
}, error = function(err) return(data.frame(mean = NA_real_))
)})
m <- lapply(m, "[", "mean")
assign(paste0("m", i), m, envir = .GlobalEnv)
m
})
The code was also wrapped in a tryCatch - in case there are some forecasting errors, it will return NA
-checking for objects in the global env
ls(pattern = '^m\\d$')
[1] "m1" "m2" "m3" "m4" "m5" "m6"