How to extract confidence intervals from modeltime recursive ensembles? - r

Question

As I want to produce some visualizations and analysis on forecasted data outside the modeltime framework, I need to extract confidence values, fitted values and maybe also residuals.
The documentation indicates, that I need to use the function modeltime_calibrate() to get the confidence values and residuals. So one question would be, where do I extract the fitted values from?
My main question is whatsoever, how to do calibration on recursive ensembles. For any non-ensemble model I was able to do it, but in case of recursive ensembles I encounter some error messages, if I want to calibrate.
To illustrate the problem, look at the example code below, which ends up failing to calibrate all models:
library(modeltime.ensemble)
library(modeltime)
library(tidymodels)
library(earth)
library(glmnet)
library(xgboost)
library(tidyverse)
library(lubridate)
library(timetk)
FORECAST_HORIZON <- 24
m4_extended <- m4_monthly %>%
group_by(id) %>%
future_frame(
.length_out = FORECAST_HORIZON,
.bind_data = TRUE
) %>%
ungroup()
lag_transformer_grouped <- function(data){
data %>%
group_by(id) %>%
tk_augment_lags(value, .lags = 1:FORECAST_HORIZON) %>%
ungroup()
}
m4_lags <- m4_extended %>%
lag_transformer_grouped()
test_data <- m4_lags %>%
group_by(id) %>%
slice_tail(n = 12) %>%
ungroup()
train_data <- m4_lags %>%
drop_na()
future_data <- m4_lags %>%
filter(is.na(value))
model_fit_glmnet <- linear_reg(penalty = 1) %>%
set_engine("glmnet") %>%
fit(value ~ ., data = train_data)
model_fit_xgboost <- boost_tree("regression", learn_rate = 0.35) %>%
set_engine("xgboost") %>%
fit(value ~ ., data = train_data)
recursive_ensemble_panel <- modeltime_table(
model_fit_glmnet,
model_fit_xgboost
) %>%
ensemble_weighted(loadings = c(4, 6)) %>%
recursive(
transform = lag_transformer_grouped,
train_tail = panel_tail(train_data, id, FORECAST_HORIZON),
id = "id"
)
model_tbl <- modeltime_table(
recursive_ensemble_panel
)
calibrated_mod <- model_tbl %>%
modeltime_calibrate(test_data, id = "id", quiet = FALSE)
model_tbl %>%
modeltime_forecast(
new_data = future_data,
actual_data = m4_lags,
keep_data = TRUE
) %>%
group_by(id) %>%
plot_modeltime_forecast(
.interactive = FALSE,
.conf_interval_show = TRUE,
.facet_ncol = 2
)

Answer 1

The problem lies in your recursive_ensemble_panel. You have to do the recursive part on the models themselves and not the ensemble. Like you I would have expected to do the recursive in one go, maybe via modeltime_table.
# start of changes to your code.
# added recursive to the model
model_fit_glmnet <- linear_reg(penalty = 1) %>%
set_engine("glmnet") %>%
fit(value ~ ., data = train_data) %>%
recursive(
transform = lag_transformer_grouped,
train_tail = panel_tail(train_data, id, FORECAST_HORIZON),
id = "id"
)
# added recursive to the model
model_fit_xgboost <- boost_tree("regression", learn_rate = 0.35) %>%
set_engine("xgboost") %>%
fit(value ~ ., data = train_data) %>%
recursive(
transform = lag_transformer_grouped,
train_tail = panel_tail(train_data, id, FORECAST_HORIZON),
id = "id"
)
# removed recursive part
recursive_ensemble_panel <- modeltime_table(
model_fit_glmnet,
model_fit_xgboost
) %>%
ensemble_weighted(loadings = c(4, 6))
# rest of your code

Answer 2

I had to do some experimentation to find the right way to extract what I need (confidence intervals and residuals).
As you can see from the example code below, there needs to be a change in the models workflow to achieve this. Recursion needs to appear in the workflow object definition and neither in the model nor in the ensemble fit/specification.
I still have to do some tests here, but I guess, that I got what I need now:
# Time Series ML
library(tidymodels)
library(modeltime)
library(modeltime.ensemble)
# Core
library(tidyverse)
library(timetk)
# data def
FORECAST_HORIZON <- 24
lag_transformer_grouped <- function(m750){
m750 %>%
group_by(id) %>%
tk_augment_lags(value, .lags = 1:FORECAST_HORIZON) %>%
ungroup()
}
m750_lags <- m750 %>%
lag_transformer_grouped()
test_data <- m750_lags %>%
group_by(id) %>%
slice_tail(n = 12) %>%
ungroup()
train_data <- m750_lags %>%
drop_na()
future_data <- m750_lags %>%
filter(is.na(value))
# rec
recipe_spec <- recipe(value ~ date, train_data) %>%
step_timeseries_signature(date) %>%
step_rm(matches("(.iso$)|(.xts$)")) %>%
step_normalize(matches("(index.num$)|(_year$)")) %>%
step_dummy(all_nominal()) %>%
step_fourier(date, K = 1, period = 12)
recipe_spec %>% prep() %>% juice()
# elnet
model_fit_glmnet <- linear_reg(penalty = 1) %>%
set_engine("glmnet")
wflw_fit_glmnet <- workflow() %>%
add_model(model_fit_glmnet) %>%
add_recipe(recipe_spec %>% step_rm(date)) %>%
fit(train_data) %>%
recursive(
transform = lag_transformer_grouped,
train_tail = panel_tail(train_data, id, FORECAST_HORIZON),
id = "id"
)
# xgboost
model_fit_xgboost <- boost_tree("regression", learn_rate = 0.35) %>%
set_engine("xgboost")
wflw_fit_xgboost <- workflow() %>%
add_model(model_fit_xgboost) %>%
add_recipe(recipe_spec %>% step_rm(date)) %>%
fit(train_data) %>%
recursive(
transform = lag_transformer_grouped,
train_tail = panel_tail(train_data, id, FORECAST_HORIZON),
id = "id"
)
# mtbl
m750_models <- modeltime_table(
wflw_fit_xgboost,
wflw_fit_glmnet
)
# mfit
ensemble_fit <- m750_models %>%
ensemble_average(type = "mean")
# mcalib
calibration_tbl <- modeltime_table(
ensemble_fit
) %>%
modeltime_calibrate(test_data)
# residuals
calib_out <- calibration_tbl$.calibration_data[[1]] %>%
left_join(test_data %>% select(id, date, value))
# Forecast ex post
ex_post_obj <-
calibration_tbl %>%
modeltime_forecast(
new_data = test_data,
actual_data = m750
)
# Forecast ex ante
data_prepared_tbl <- bind_rows(train_data, test_data)
future_tbl <- data_prepared_tbl %>%
group_by(id) %>%
future_frame(.length_out = "2 years") %>%
ungroup()
ex_ante_obj <-
calibration_tbl %>%
modeltime_forecast(
new_data = future_tbl,
actual_data = m750
)