Comments (1)
Hi @luzbarbosa ,
Could you provide a reproducible example?
It is very complicated to be able to help you without knowing how you have generated the resamples or how you have reached those results.
In the tests that I have done, I do not see that the variable is lagging and both show that they are identical. Here is the code I used to test it in case it could be of any use to you:
library(tidymodels)
library(modeltime)
library(modeltime.resample)
library(timetk)
library(tidyverse)
library(tidyquant)
full_data_tbl <- walmart_sales_weekly %>%
select(id, Date, Weekly_Sales) %>%
# Apply Group-wise Time Series Manipulations
group_by(id) %>%
future_frame(
.date_var = Date,
.length_out = "3 months",
.bind_data = TRUE
) %>%
ungroup() %>%
# Consolidate IDs
mutate(id = fct_drop(id))
# Training Data
data_prepared_tbl <- full_data_tbl %>%
filter(!is.na(Weekly_Sales))
# Forecast Data
future_tbl <- full_data_tbl %>%
filter(is.na(Weekly_Sales))
walmart_tscv <- data_prepared_tbl %>%
time_series_cv(
date_var = Date,
assess = "3 months",
skip = "3 months",
cumulative = TRUE,
slice_limit = 6
)
walmart_tscv %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(Date, Weekly_Sales,
.facet_ncol = 2, .interactive = F)
recipe_spec <- recipe(Weekly_Sales ~ .,
data = training(walmart_tscv$splits[[1]])) %>%
step_timeseries_signature(Date) %>%
step_rm(matches("(.iso$)|(.xts$)|(day)|(hour)|(minute)|(second)|(am.pm)")) %>%
step_mutate(Date_week = factor(Date_week, ordered = TRUE)) %>%
step_dummy(all_nominal(), one_hot = TRUE)
wflw_fit_xgboost <- workflow() %>%
add_model(
boost_tree() %>% set_engine("xgboost")
) %>%
add_recipe(recipe_spec %>% step_rm(Date)) %>%
fit(training(walmart_tscv$splits[[1]]))
model_tbl <- modeltime_table(
wflw_fit_xgboost
)
resample_results <- model_tbl %>%
modeltime_fit_resamples(
resamples = walmart_tscv,
control = control_resamples(verbose = FALSE)
)
input <- walmart_tscv %>%
filter(id == "Slice1") %>%
pull(splits) %>%
pluck(1) %>%
training()
input_2<-resample_results$.resample_results[[1]] %>%
filter(id == "Slice1") %>%
pull(splits) %>%
pluck(1) %>%
training()
identical(input, input_2)
In case you need more help, please send a reprex.
Regards,
from modeltime.resample.
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from modeltime.resample.