diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 61589d1d..9e9894e7 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -51,7 +51,7 @@ jobs:
           
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::rcmdcheck, vip=?ignore-before-r=4.1.0, Boruta=?ignore-before-r=4.1.0
+          extra-packages: any::rcmdcheck, vip=?ignore-before-r=4.1.0, Boruta=?ignore-before-r=4.1.0, corrr=?ignore-before-r=4.1.0
           needs: check
 
       - uses: r-lib/actions/check-r-package@v2
diff --git a/DESCRIPTION b/DESCRIPTION
index 99ff60e9..0f210549 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: finnts
 Title: Microsoft Finance Time Series Forecasting Framework
-Version: 0.4.0.9003
+Version: 0.4.0.9004
 Authors@R: 
     c(person(given = "Mike",
            family = "Tokic",
diff --git a/NEWS.md b/NEWS.md
index c5a6999a..444a0ad6 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,8 +1,9 @@
-# finnts 0.4.0.9003 (DEVELOPMENT VERSION)
+# finnts 0.4.0.9004 (DEVELOPMENT VERSION)
 
 ## Improvements
 
 -   Added support for hierarchical forecasting with external regressors
+-   Allow global models for hierarchical forecasts
 -   Multistep horizon forecasts for R1 recipe, listed as `multistep_horizon` within `prep_data()` 
 
 ## Bug Fixes
diff --git a/R/multistep_cubist.R b/R/multistep_cubist.R
index efd7f99b..8a005a4c 100644
--- a/R/multistep_cubist.R
+++ b/R/multistep_cubist.R
@@ -439,6 +439,13 @@ predict.cubist_multistep_fit_impl <- function(object, new_data, ...) {
 #' @export
 cubist_multistep_predict_impl <- function(object, new_data, ...) {
 
+  # Date Mapping Table
+  date_tbl <- new_data %>%
+    dplyr::select(Date, Date_index.num) %>%
+    dplyr::distinct() %>%
+    dplyr::arrange(Date) %>%
+    dplyr::mutate(Run_Number = dplyr::row_number())
+
   # PREPARE INPUTS
   xreg_recipe <- object$extras$xreg_recipe
   h_horizon <- nrow(new_data)
@@ -447,14 +454,16 @@ cubist_multistep_predict_impl <- function(object, new_data, ...) {
   xreg_tbl <- modeltime::bake_xreg_recipe(xreg_recipe,
     new_data,
     format = "tbl"
-  )
+  ) %>%
+    dplyr::left_join(date_tbl, by = "Date_index.num") %>%
+    dplyr::mutate(Row_Num = dplyr::row_number())
 
   # PREDICTIONS
-  final_prediction <- c()
+  final_prediction <- tibble::tibble()
   start_val <- 1
 
   for (model_name in names(object$models)) {
-    if (start_val > nrow(xreg_tbl)) {
+    if (start_val > nrow(date_tbl)) {
       break
     }
 
@@ -463,7 +472,10 @@ cubist_multistep_predict_impl <- function(object, new_data, ...) {
     cubist_model <- object$models[[model_name]]
 
     xreg_tbl_final <- xreg_tbl %>%
-      dplyr::slice(start_val:lag_number)
+      dplyr::filter(
+        Run_Number >= start_val,
+        Run_Number <= lag_number
+      )
 
     if (!is.null(xreg_tbl)) {
       preds_cubist <- predict(cubist_model, xreg_tbl_final)
@@ -471,9 +483,17 @@ cubist_multistep_predict_impl <- function(object, new_data, ...) {
       preds_cubist <- rep(0, h_horizon)
     }
 
+    preds_cubist <- tibble::tibble(.pred = preds_cubist) %>%
+      dplyr::mutate(Row_Num = xreg_tbl_final$Row_Num)
+
     start_val <- as.numeric(lag_number) + 1
-    final_prediction <- c(final_prediction, preds_cubist)
+    final_prediction <- rbind(final_prediction, preds_cubist)
   }
 
+  # Ensure it's sorted correctly for global models
+  final_prediction <- final_prediction %>%
+    dplyr::arrange(Row_Num) %>%
+    dplyr::pull(.pred)
+
   return(final_prediction)
 }
diff --git a/R/multistep_glmnet.R b/R/multistep_glmnet.R
index 0ab6f155..f177aaa0 100644
--- a/R/multistep_glmnet.R
+++ b/R/multistep_glmnet.R
@@ -427,6 +427,13 @@ predict.glmnet_multistep_fit_impl <- function(object, new_data, ...) {
 #' @export
 glmnet_multistep_predict_impl <- function(object, new_data, ...) {
 
+  # Date Mapping Table
+  date_tbl <- new_data %>%
+    dplyr::select(Date, Date_index.num) %>%
+    dplyr::distinct() %>%
+    dplyr::arrange(Date) %>%
+    dplyr::mutate(Run_Number = dplyr::row_number())
+
   # PREPARE INPUTS
   xreg_recipe <- object$extras$xreg_recipe
   h_horizon <- nrow(new_data)
@@ -435,14 +442,16 @@ glmnet_multistep_predict_impl <- function(object, new_data, ...) {
   xreg_tbl <- modeltime::bake_xreg_recipe(xreg_recipe,
     new_data,
     format = "tbl"
-  )
+  ) %>%
+    dplyr::left_join(date_tbl, by = "Date_index.num") %>%
+    dplyr::mutate(Row_Num = dplyr::row_number())
 
   # PREDICTIONS
   final_prediction <- tibble::tibble()
   start_val <- 1
 
   for (model_name in names(object$models)) {
-    if (start_val > nrow(xreg_tbl)) {
+    if (start_val > nrow(date_tbl)) {
       break
     }
 
@@ -451,7 +460,10 @@ glmnet_multistep_predict_impl <- function(object, new_data, ...) {
     glmnet_model <- object$models[[model_name]]
 
     xreg_tbl_final <- xreg_tbl %>%
-      dplyr::slice(start_val:lag_number)
+      dplyr::filter(
+        Run_Number >= start_val,
+        Run_Number <= lag_number
+      )
 
     if (!is.null(xreg_tbl)) {
       preds_glmnet <- predict(glmnet_model, xreg_tbl_final)
@@ -459,9 +471,17 @@ glmnet_multistep_predict_impl <- function(object, new_data, ...) {
       preds_glmnet <- rep(0, h_horizon)
     }
 
+    preds_glmnet <- preds_glmnet %>%
+      dplyr::mutate(Row_Num = xreg_tbl_final$Row_Num)
+
     start_val <- as.numeric(lag_number) + 1
     final_prediction <- rbind(final_prediction, preds_glmnet)
   }
 
+  # Ensure it's sorted correctly for global models
+  final_prediction <- final_prediction %>%
+    dplyr::arrange(Row_Num) %>%
+    dplyr::select(.pred)
+
   return(final_prediction)
 }
diff --git a/R/multistep_mars.R b/R/multistep_mars.R
index b4f04f06..02e84e5b 100644
--- a/R/multistep_mars.R
+++ b/R/multistep_mars.R
@@ -450,6 +450,13 @@ predict.mars_multistep_fit_impl <- function(object, new_data, ...) {
 #' @export
 mars_multistep_predict_impl <- function(object, new_data, ...) {
 
+  # Date Mapping Table
+  date_tbl <- new_data %>%
+    dplyr::select(Date, Date_index.num) %>%
+    dplyr::distinct() %>%
+    dplyr::arrange(Date) %>%
+    dplyr::mutate(Run_Number = dplyr::row_number())
+
   # PREPARE INPUTS
   xreg_recipe <- object$extras$xreg_recipe
   h_horizon <- nrow(new_data)
@@ -458,14 +465,16 @@ mars_multistep_predict_impl <- function(object, new_data, ...) {
   xreg_tbl <- modeltime::bake_xreg_recipe(xreg_recipe,
     new_data,
     format = "tbl"
-  )
+  ) %>%
+    dplyr::left_join(date_tbl, by = "Date_index.num") %>%
+    dplyr::mutate(Row_Num = dplyr::row_number())
 
   # PREDICTIONS
   final_prediction <- tibble::tibble()
   start_val <- 1
 
   for (model_name in names(object$models)) {
-    if (start_val > nrow(xreg_tbl)) {
+    if (start_val > nrow(date_tbl)) {
       break
     }
 
@@ -474,7 +483,10 @@ mars_multistep_predict_impl <- function(object, new_data, ...) {
     mars_model <- object$models[[model_name]]
 
     xreg_tbl_final <- xreg_tbl %>%
-      dplyr::slice(start_val:lag_number)
+      dplyr::filter(
+        Run_Number >= start_val,
+        Run_Number <= lag_number
+      )
 
     if (!is.null(xreg_tbl)) {
       preds_mars <- predict(mars_model, xreg_tbl_final)
@@ -482,9 +494,17 @@ mars_multistep_predict_impl <- function(object, new_data, ...) {
       preds_mars <- rep(0, h_horizon)
     }
 
+    preds_mars <- preds_mars %>%
+      dplyr::mutate(Row_Num = xreg_tbl_final$Row_Num)
+
     start_val <- as.numeric(lag_number) + 1
     final_prediction <- rbind(final_prediction, preds_mars)
   }
 
+  # Ensure it's sorted correctly for global models
+  final_prediction <- final_prediction %>%
+    dplyr::arrange(Row_Num) %>%
+    dplyr::select(.pred)
+
   return(final_prediction)
 }
diff --git a/R/multistep_svm_poly.R b/R/multistep_svm_poly.R
index ac082a72..14ad5e21 100644
--- a/R/multistep_svm_poly.R
+++ b/R/multistep_svm_poly.R
@@ -476,6 +476,13 @@ predict.svm_poly_multistep_fit_impl <- function(object, new_data, ...) {
 #' @export
 svm_poly_multistep_predict_impl <- function(object, new_data, ...) {
 
+  # Date Mapping Table
+  date_tbl <- new_data %>%
+    dplyr::select(Date, Date_index.num) %>%
+    dplyr::distinct() %>%
+    dplyr::arrange(Date) %>%
+    dplyr::mutate(Run_Number = dplyr::row_number())
+
   # PREPARE INPUTS
   xreg_recipe <- object$extras$xreg_recipe
   h_horizon <- nrow(new_data)
@@ -484,14 +491,16 @@ svm_poly_multistep_predict_impl <- function(object, new_data, ...) {
   xreg_tbl <- modeltime::bake_xreg_recipe(xreg_recipe,
     new_data,
     format = "tbl"
-  )
+  ) %>%
+    dplyr::left_join(date_tbl, by = "Date_index.num") %>%
+    dplyr::mutate(Row_Num = dplyr::row_number())
 
   # PREDICTIONS
   final_prediction <- tibble::tibble()
   start_val <- 1
 
   for (model_name in names(object$models)) {
-    if (start_val > nrow(xreg_tbl)) {
+    if (start_val > nrow(date_tbl)) {
       break
     }
 
@@ -500,7 +509,10 @@ svm_poly_multistep_predict_impl <- function(object, new_data, ...) {
     svm_poly_model <- object$models[[model_name]]
 
     xreg_tbl_final <- xreg_tbl %>%
-      dplyr::slice(start_val:lag_number)
+      dplyr::filter(
+        Run_Number >= start_val,
+        Run_Number <= lag_number
+      )
 
     if (!is.null(xreg_tbl)) {
       preds_svm_poly <- predict(svm_poly_model, xreg_tbl_final)
@@ -508,9 +520,17 @@ svm_poly_multistep_predict_impl <- function(object, new_data, ...) {
       preds_svm_poly <- rep(0, h_horizon)
     }
 
+    preds_svm_poly <- preds_svm_poly %>%
+      dplyr::mutate(Row_Num = xreg_tbl_final$Row_Num)
+
     start_val <- as.numeric(lag_number) + 1
     final_prediction <- rbind(final_prediction, preds_svm_poly)
   }
 
+  # Ensure it's sorted correctly for global models
+  final_prediction <- final_prediction %>%
+    dplyr::arrange(Row_Num) %>%
+    dplyr::select(.pred)
+
   return(final_prediction)
 }
diff --git a/R/multistep_svm_rbf.R b/R/multistep_svm_rbf.R
index 17a4c621..6a7f779f 100644
--- a/R/multistep_svm_rbf.R
+++ b/R/multistep_svm_rbf.R
@@ -456,6 +456,13 @@ predict.svm_rbf_multistep_fit_impl <- function(object, new_data, ...) {
 #' @export
 svm_rbf_multistep_predict_impl <- function(object, new_data, ...) {
 
+  # Date Mapping Table
+  date_tbl <- new_data %>%
+    dplyr::select(Date, Date_index.num) %>%
+    dplyr::distinct() %>%
+    dplyr::arrange(Date) %>%
+    dplyr::mutate(Run_Number = dplyr::row_number())
+
   # PREPARE INPUTS
   xreg_recipe <- object$extras$xreg_recipe
   h_horizon <- nrow(new_data)
@@ -464,14 +471,16 @@ svm_rbf_multistep_predict_impl <- function(object, new_data, ...) {
   xreg_tbl <- modeltime::bake_xreg_recipe(xreg_recipe,
     new_data,
     format = "tbl"
-  )
+  ) %>%
+    dplyr::left_join(date_tbl, by = "Date_index.num") %>%
+    dplyr::mutate(Row_Num = dplyr::row_number())
 
   # PREDICTIONS
   final_prediction <- tibble::tibble()
   start_val <- 1
 
   for (model_name in names(object$models)) {
-    if (start_val > nrow(xreg_tbl)) {
+    if (start_val > nrow(date_tbl)) {
       break
     }
 
@@ -480,7 +489,10 @@ svm_rbf_multistep_predict_impl <- function(object, new_data, ...) {
     svm_rbf_model <- object$models[[model_name]]
 
     xreg_tbl_final <- xreg_tbl %>%
-      dplyr::slice(start_val:lag_number)
+      dplyr::filter(
+        Run_Number >= start_val,
+        Run_Number <= lag_number
+      )
 
     if (!is.null(xreg_tbl)) {
       preds_svm_rbf <- predict(svm_rbf_model, xreg_tbl_final)
@@ -488,9 +500,17 @@ svm_rbf_multistep_predict_impl <- function(object, new_data, ...) {
       preds_svm_rbf <- rep(0, h_horizon)
     }
 
+    preds_svm_rbf <- preds_svm_rbf %>%
+      dplyr::mutate(Row_Num = xreg_tbl_final$Row_Num)
+
     start_val <- as.numeric(lag_number) + 1
     final_prediction <- rbind(final_prediction, preds_svm_rbf)
   }
 
+  # Ensure it's sorted correctly for global models
+  final_prediction <- final_prediction %>%
+    dplyr::arrange(Row_Num) %>%
+    dplyr::select(.pred)
+
   return(final_prediction)
 }
diff --git a/R/multistep_xgboost.R b/R/multistep_xgboost.R
index a933635d..03b327f1 100644
--- a/R/multistep_xgboost.R
+++ b/R/multistep_xgboost.R
@@ -538,6 +538,13 @@ predict.xgboost_multistep_fit_impl <- function(object, new_data, ...) {
 #' @export
 xgboost_multistep_predict_impl <- function(object, new_data, ...) {
 
+  # Date Mapping Table
+  date_tbl <- new_data %>%
+    dplyr::select(Date, Date_index.num) %>%
+    dplyr::distinct() %>%
+    dplyr::arrange(Date) %>%
+    dplyr::mutate(Run_Number = dplyr::row_number())
+
   # PREPARE INPUTS
   xreg_recipe <- object$extras$xreg_recipe
   h_horizon <- nrow(new_data)
@@ -546,14 +553,16 @@ xgboost_multistep_predict_impl <- function(object, new_data, ...) {
   xreg_tbl <- modeltime::bake_xreg_recipe(xreg_recipe,
     new_data,
     format = "tbl"
-  )
+  ) %>%
+    dplyr::left_join(date_tbl, by = "Date_index.num") %>%
+    dplyr::mutate(Row_Num = dplyr::row_number())
 
   # PREDICTIONS
-  final_prediction <- c()
+  final_prediction <- tibble::tibble()
   start_val <- 1
 
   for (model_name in names(object$models)) {
-    if (start_val > nrow(xreg_tbl)) {
+    if (start_val > nrow(date_tbl)) {
       break
     }
 
@@ -561,9 +570,14 @@ xgboost_multistep_predict_impl <- function(object, new_data, ...) {
 
     xgboost_model <- object$models[[model_name]]
 
-    xreg_tbl_final <- xreg_tbl %>%
-      dplyr::select(tidyselect::any_of(xgboost_model$feature_names)) %>%
-      dplyr::slice(start_val:lag_number)
+    xreg_tbl_temp <- xreg_tbl %>%
+      dplyr::filter(
+        Run_Number >= start_val,
+        Run_Number <= lag_number
+      )
+
+    xreg_tbl_final <- xreg_tbl_temp %>%
+      dplyr::select(tidyselect::any_of(xgboost_model$feature_names))
 
     if (!is.null(xreg_tbl)) {
       preds_xgboost <- modeltime::xgboost_predict(xgboost_model,
@@ -574,9 +588,17 @@ xgboost_multistep_predict_impl <- function(object, new_data, ...) {
       preds_xgboost <- rep(0, h_horizon)
     }
 
+    preds_xgboost <- tibble::tibble(.pred = preds_xgboost) %>%
+      dplyr::mutate(Row_Num = xreg_tbl_temp$Row_Num)
+
     start_val <- as.numeric(lag_number) + 1
-    final_prediction <- c(final_prediction, preds_xgboost)
+    final_prediction <- rbind(final_prediction, preds_xgboost)
   }
 
+  # Ensure it's sorted correctly for global models
+  final_prediction <- final_prediction %>%
+    dplyr::arrange(Row_Num) %>%
+    dplyr::pull(.pred)
+
   return(final_prediction)
 }
diff --git a/R/train_models.R b/R/train_models.R
index 54521aa5..511c9383 100644
--- a/R/train_models.R
+++ b/R/train_models.R
@@ -98,8 +98,6 @@ train_models <- function(run_info,
 
   if (is.null(run_global_models) & date_type %in% c("day", "week")) {
     run_global_models <- FALSE
-  } else if (forecast_approach != "bottoms_up") {
-    run_global_models <- FALSE
   } else if (is.null(run_global_models)) {
     run_global_models <- TRUE
   } else {
@@ -652,7 +650,7 @@ train_models <- function(run_info,
         tidyr::unnest(Forecast_Tbl) %>%
         dplyr::arrange(Train_Test_ID) %>%
         tidyr::unite(col = "Model_ID", c("Model_Name", "Model_Type", "Recipe_ID"), sep = "--", remove = FALSE) %>%
-        dplyr::group_by(Combo_ID, Model_ID, Train_Test_ID) %>%
+        dplyr::group_by(Combo, Model_ID, Train_Test_ID) %>%
         dplyr::mutate(Horizon = dplyr::row_number()) %>%
         dplyr::ungroup()
 
diff --git a/R/utility.R b/R/utility.R
index 1da63edf..cb5a57d5 100644
--- a/R/utility.R
+++ b/R/utility.R
@@ -15,7 +15,7 @@ utils::globalVariables(c(
   "Auto_Accept", "Feature", "Imp", "Importance", "LOFO_Var", "Var_RMSE", "Vote", "Votes", "desc",
   "term", "Column", "Box_Cox_Lambda", "get_recipie_configurable", "Agg", "Unique", "Var",
   "Var_Combo", "regressor", "regressor_tbl", "value_level_iter", ".actual", ".fitted",
-  "forecast_horizon", "lag", "new_data", "object", "fit"
+  "forecast_horizon", "lag", "new_data", "object", "fit", "Row_Num", "Run_Number"
 ))
 
 #' @importFrom magrittr %>%