Train time series models in caret by group - r

Question

I have a data set like the following
set.seed(503)
foo <- data.table(group = rep(LETTERS[1:6], 150),
y = rnorm(n = 6 * 150, mean = 5, sd = 2),
x1 = rnorm(n = 6 * 150, mean = 5, sd = 10),
x2 = rnorm(n = 6 * 150, mean = 25, sd = 10),
x3 = rnorm(n = 6 * 150, mean = 50, sd = 10),
x4 = rnorm(n = 6 * 150, mean = 0.5, sd = 10),
x5 = sample(c(1, 0), size = 6 * 150, replace = T))
foo[, period := 1:.N, by = group]
Problem: I want to forecast y one step ahead, for each group, using variables x1, ..., x5
I want to run a few models in caret to decide which I will use.
As of now, I am running it in a loop using timeslice
window.length <- 115
timecontrol <- trainControl(method = 'timeslice',
initialWindow = window.length,
horizon = 1,
selectionFunction = "best",
fixedWindow = TRUE,
savePredictions = 'final')
model_list <- list()
for(g in unique(foo$group)){
for(model in c("xgbTree", "earth", "cubist")){
dat <- foo[group == g][, c('group', 'period') := NULL]
model_list[[g]][[model]] <- train(y ~ . - 1,
data = dat,
method = model,
trControl = timecontrol)
}
}
However, I would like to run all groups at the same time, using dummy variables to identify each one, like
dat <- cbind(foo, model.matrix(~ group- 1, foo))
y x1 x2 x3 x4 x5 period groupA groupB groupC groupD groupE groupF
1: 5.710250 11.9615460 22.62916 31.04790 -4.821331e-04 1 1 1 0 0 0 0 0
2: 3.442213 8.6558983 32.41881 45.70801 3.255423e-01 1 1 0 1 0 0 0 0
3: 3.485286 7.7295448 21.99022 56.42133 8.668391e+00 1 1 0 0 1 0 0 0
4: 9.659601 0.9166456 30.34609 55.72661 -7.666063e+00 1 1 0 0 0 1 0 0
5: 5.567950 3.0306864 22.07813 52.21099 5.377153e-01 1 1 0 0 0 0 1 0
But still running the time series with the correct time ordering using timeslice.
Is there a way to declare the time variable in trainControl, so my one step ahead forecast uses, in this case, six more observations for each round and droping the first 6 observations?
I can do it by ordering the data and messing with the horizon argument (given n groups, order by the time variable and put horizon = n), but this has to change if the number of groups change. And initial.window will have to be time * n_groups
timecontrol <- trainControl(method = 'timeslice',
initialWindow = window.length * length(unique(foo$group)),
horizon = length(unique(foo$group)),
selectionFunction = "best",
fixedWindow = TRUE,
savePredictions = 'final')
Is there any ohter way?

Answer 1

I think the answer you are looking for is actually quite simple. You can use the skip argument to trainControl() to skip the desired number of observations after each train/test set. In this way, you only predict each group-period once, the same period is never split between the training group and testing group, and there is no information leakage.
Using the example you provided, if you set skip = 6 and horizon = 6 (the number of groups), and initialWindow = 115, then the first test set will include all groups for period 116, the next test set will include all groups for period 117, and so on.
library(caret)
library(tidyverse)
set.seed(503)
foo <- tibble(group = rep(LETTERS[1:6], 150),
y = rnorm(n = 6 * 150, mean = 5, sd = 2),
x1 = rnorm(n = 6 * 150, mean = 5, sd = 10),
x2 = rnorm(n = 6 * 150, mean = 25, sd = 10),
x3 = rnorm(n = 6 * 150, mean = 50, sd = 10),
x4 = rnorm(n = 6 * 150, mean = 0.5, sd = 10),
x5 = sample(c(1, 0), size = 6 * 150, replace = T)) %>%
group_by(group) %>%
mutate(period = row_number()) %>%
ungroup()
dat <- cbind(foo, model.matrix(~ group- 1, foo)) %>%
select(-group)
window.length <- 115
timecontrol <- trainControl(
method = 'timeslice',
initialWindow = window.length * length(unique(foo$group)),
horizon = length(unique(foo$group)),
skip = length(unique(foo$group)),
selectionFunction = "best",
fixedWindow = TRUE,
savePredictions = 'final'
)
model_names <- c("xgbTree", "earth", "cubist")
fits <- map(model_names,
~ train(
y ~ . - 1,
data = dat,
method = .x,
trControl = timecontrol
)) %>%
set_names(model_names)

Answer 2

I would use tidyr::nest() to nest groups and then iterate over the data with purrr::map(). This approach is much more flexible because it can accommodate different group sizes, different numbers of groups, and variable models or other arguments passed to caret::train(). Also, you can easily run everything in parallel using furrr.
Load packages and create data
I use tibble instead of data.table. I also reduce the size of the data.
library(caret)
library(tidyverse)
set.seed(503)
foo <- tibble(
group = rep(LETTERS[1:6], 10),
y = rnorm(n = 6 * 10, mean = 5, sd = 2),
x1 = rnorm(n = 6 * 10, mean = 5, sd = 10),
x2 = rnorm(n = 6 * 10, mean = 25, sd = 10),
x3 = rnorm(n = 6 * 10, mean = 50, sd = 10),
x4 = rnorm(n = 6 * 10, mean = 0.5, sd = 10),
x5 = sample(c(1, 0), size = 6 * 10, replace = T)
) %>%
group_by(group) %>%
mutate(period = row_number()) %>%
ungroup()
Reduce initialWindow size
window.length <- 9
timecontrol <- trainControl(
method = 'timeslice',
initialWindow = window.length,
horizon = 1,
selectionFunction = "best",
fixedWindow = TRUE,
savePredictions = 'final'
)
Create a function that will return a list of fit model objects
# To fit each model in model_list to data and return model fits as a list.
fit_models <- function(data, model_list, timecontrol) {
map(model_list,
~ train(
y ~ . - 1,
data = data,
method = .x,
trControl = timecontrol
)) %>%
set_names(model_list)
}
Fit models
model_list <- c("xgbTree", "earth", "cubist")
mods <- foo %>%
nest(-group)
mods <- mods %>%
mutate(fits = map(
data,
~ fit_models(
data = .x,
model_list = model_list,
timecontrol = timecontrol
)
))
If you want to view the results for a particular group / model you can do:
mods[which(mods$group == "A"), ]$fits[[1]]$xgbTree
Use furrr for parallel processing
Just initialize workers with plan(multiprocess) and change map to future_map. Note you might want to change the number of workers to something less than 6 if your computer has fewer than 6 processing cores.
library(furrr)
plan(multiprocess, workers = 6)
mods <- foo %>%
nest(-group)
mods <- mods %>%
mutate(fits = future_map(
data,
~ fit_models(
data = .x,
model_list = model_list,
timecontrol = timecontrol
)
))