如何从模型时间递归集成中提取置信区间？

Question

由于我想在模型时间框架之外对预测数据进行一些可视化和分析，因此我需要提取置信度值、拟合值，也许还有残差。

文档表明，我需要使用函数 modeltime_calibrate() 来获取置信度值和残差。所以一个问题是，我从哪里提取拟合值？

我的主要问题是，如何对递归集成进行校准。对于任何非集成模型，我都能做到，但在递归集成的情况下，如果我想校准，我会遇到一些错误消息。

为了说明问题，请看下面的示例代码，它最终无法校准所有模型：

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

我不得不做一些实验来找到正确的方法来提取我需要的东西（置信区间和残差）。

您可以从下面的示例代码中看到，需要对模型工作流程进行更改才能实现此目的。递归需要出现在工作流对象定义中，既不在模型中，也不在集成拟合/规范中。

我仍然需要在这里做一些测试，但我想，我知道了我需要知道的：

# 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
  )

Answer 2

问题在于您的recursive_ensemble_panel。您必须对模型本身而不是整体进行递归部分。像你一样，我本来希望一次完成递归，也许是通过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