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$subproduct))) {
#assign(paste0("df", i), as.data.frame(dummy_list[[i]]))
list_dfs <-append(list_dfs,dummy_list[[i]])
}
combined_dfs <- Reduce(function(x, y) merge(x, y, all = TRUE,
by='date'), list(list_dfs))
Creating the time series
list_ts <- lapply(list_dfs, function(t)
ts(t,start=c(2019,1),end=c(2021,6), frequency = 12)) %>%
lapply( function(t) ts_split(t,sample.out=(0.2*length(t)))) #
creates my train test split
list_ts <- do.call("rbind", list_ts) #Creates a list of time series
Question. This doesn't give me more than 9 models. I'd want a model for n1 =.1 n2=.99 and n3= .3 for example so we would have way more than 9 models for this.
n1 <- seq(0.1, 0.99, by = 0.1)
n2 <- seq(0.1, 0.99, by = 0.1)
n3 <- seq(0.1, 0.99, by = 0.1)
out<- lapply(seq_along(n1), function(i) {
cw_triple_holtwinters_additive <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(ses(x,h=24,alpha =
n1[i],beta=n2[i],gamma=n3[i])))
cw_triple_holtwinters_additive <-
lapply(cw_triple_holtwinters_additive, "[", "mean")
assign(paste0("cw_triple_holtwinters_additive", i),
cw_triple_holtwinters_additive, envir = .GlobalEnv)
cw_triple_holtwinters_additive})
Additional question: for order=c(1,1,1) and order=c(0,1,0) can I create a list of values like these and loop through them both at the same time like Akrun's solution?
cw_seasonal_autoregressive_integratedmovingaverage1 <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(1,1,1),seasonal=list(order=c(0,1,0),
period=12)) ,h=24))
cw_seasonal_autoregressive_integratedmovingaverage1 <-
lapply(cw_seasonal_autoregressive_integratedmovingaverage1, "[",
c("mean"))
We can use expand.grid to get all the combinations
dat_n <- expand.grid(n1 = n1, n2= n2, n3 = n3)
Then, we loop over the sequence of rows of 'dat_n'
out<- lapply(seq_len(nrow(dat_n)), function(i) {
cw_triple_holtwinters_additive <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(ses(x,h=24,alpha =
dat_n$n1[i],beta=dat_n$n2[i],gamma=dat_n$n3[i])))
cw_triple_holtwinters_additive <-
lapply(cw_triple_holtwinters_additive, "[", "mean")
assign(paste0("cw_triple_holtwinters_additive", i),
cw_triple_holtwinters_additive, envir = .GlobalEnv)
cw_triple_holtwinters_additive})
-checking
ls(pattern = "cw_triple")
[1] "cw_triple_holtwinters_additive1" "cw_triple_holtwinters_additive10" "cw_triple_holtwinters_additive100" "cw_triple_holtwinters_additive101"
[5] "cw_triple_holtwinters_additive102" "cw_triple_holtwinters_additive103" "cw_triple_holtwinters_additive104" "cw_triple_holtwinters_additive105"
[9] "cw_triple_holtwinters_additive106" "cw_triple_holtwinters_additive107" "cw_triple_holtwinters_additive108" "cw_triple_holtwinters_additive109"
[13] "cw_triple_holtwinters_additive11" "cw_triple_holtwinters_additive110" "cw_triple_holtwinters_additive111" "cw_triple_holtwinters_additive112"
[17] "cw_triple_holtwinters_additive113" "cw_triple_holtwinters_additive114" "cw_triple_holtwinters_additive115" "cw_triple_holtwinters_additive116"
[21] "cw_triple_holtwinters_additive117"
...