diff --git a/DESCRIPTION b/DESCRIPTION
index bfe9758..c100cfe 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: tswgewrapped
 Title: Helpful wrappers for 'tswge', 'vars' and 'nnfor' time series packages
-Version: 1.8.10.5
+Version: 1.8.10.6
 Authors@R: c(
     person("David", "Josephs", email = "josephsd@smu.edu", role = c("aut", "cre")),
     person("Nikhil", "Gupta", email = "guptan@smu.edu", role = c("aut")))
@@ -31,6 +31,7 @@ Imports:
     Rfast,
     tibble,
     tictoc,
+    tidyr (>= 1.0.0),
     tswge,
     vars
 RoxygenNote: 7.1.0
diff --git a/R/ModelCombine.R b/R/ModelCombine.R
index 83731a6..18ffcbc 100644
--- a/R/ModelCombine.R
+++ b/R/ModelCombine.R
@@ -22,6 +22,7 @@ ModelCombine = R6::R6Class(
     #' @return A new `ModelCombine` object.
     initialize = function(data = NA, var_interest = NA, uni_models = NA, var_models = NA, mlp_models = NA, verbose = 0)
     {
+      self$set_verbose(verbose = verbose)
       private$set_data(data = data)
       private$set_var_interest(var_interest = var_interest)
       private$set_uni_compare_objects(models = uni_models)
@@ -51,7 +52,61 @@ ModelCombine = R6::R6Class(
     set_verbose = function(verbose = 0){ private$verbose = verbose },
     
     #### General Public Methods ----
-   
+    
+    #' @description Plots the simple forecast for each model
+    #' @param lastn If TRUE, this will plot the forecasts forthe last n.ahead values of the realization (Default: FALSE)
+    #' @param newxreg The future exogenous variable values to be used for prediction.
+    #'                Applicable to models that require the values of the new exogenous variables to be provided for future forecasts, e.g. nnfor::mlp()
+    #' @param limits If TRUE, this will also plot the lower and upper limits of the forecasts (Default: FALSE)
+    #' @param zoom A number indicating how much to zoom into the plot. 
+    #'             For example zoom = 50 will only plot the last 50 points of the realization
+    #'             Useful for cases where realizations that are long and n.ahead is small.
+    plot_simple_forecasts = function(lastn = FALSE, newxreg = NA, limits = FALSE, zoom = NA){
+      
+      forecasts = data.frame()
+      
+      mlp_compare_objects = private$get_mlp_compare_objects()
+      if (length(mlp_compare_objects) >= 1 & lastn == TRUE){
+        stop(paste0("Your '", self$classname, "' object has a ModelCompareNNforCaret object which does not support plotting simple forecasts with lastn = TRUE. Please make lastn = FALSE and rerun with xreg passed."))
+      }
+      for (i in seq_along(mlp_compare_objects)){
+        subset_results = mlp_compare_objects[[i]]$plot_simple_forecasts(lastn = lastn, newxreg = newxreg, limits = limits, zoom = zoom, plot = FALSE)
+        
+        filtered = subset_results$plot_data %>%  
+          private$filter_best_caret_model(caret_compare_object = mlp_compare_objects[[i]])
+        
+        forecasts = dplyr::bind_rows(forecasts, filtered) 
+      }
+      
+      uni_compare_objects = private$get_uni_compare_objects()
+      for (i in seq_along(uni_compare_objects)){
+        subset_results = uni_compare_objects[[i]]$plot_simple_forecasts(lastn = lastn, newxreg = newxreg, limits = limits, zoom = zoom, plot = FALSE)
+        forecasts = dplyr::bind_rows(forecasts, subset_results$plot_data)
+      }
+      
+      var_compare_objects = private$get_var_compare_objects()
+      for (i in seq_along(var_compare_objects)){
+        subset_results = var_compare_objects[[i]]$plot_simple_forecasts(lastn = lastn, newxreg = newxreg, limits = limits, zoom = zoom, plot = FALSE)
+        forecasts = dplyr::bind_rows(forecasts, subset_results$plot_data)
+      }
+      
+      p = ggplot2::ggplot() +
+        ggplot2::geom_line(forecasts %>% dplyr::filter(Model == "Actual"), mapping = ggplot2::aes(x=Time, y=f, color = Model), size = 1) +
+        ggplot2::geom_line(forecasts %>% dplyr::filter(Model != "Actual"), mapping = ggplot2::aes(x=Time, y=f, color = Model), size = 0.75) +
+        ggplot2::ylab("Simple Forecasts")
+      
+      if (limits == TRUE){
+        p = p + 
+          ggplot2::geom_line(forecasts, mapping = ggplot2::aes(x=Time, y=ll, color = Model), linetype = "dashed", size = 0.5) +
+          ggplot2::geom_line(forecasts, mapping = ggplot2::aes(x=Time, y=ul, color = Model), linetype = "dashed", size = 0.5)
+      }
+      
+      print(p)
+      
+      return(forecasts)
+
+    },
+    
     #' @description Plots the forecasts per batch for all models
     #' @param only_sliding If TRUE, this will only plot the batch forecasts 
     #'                     for the models that used window ASE calculations
@@ -231,22 +286,110 @@ ModelCombine = R6::R6Class(
         subset_results = mlp_compare_objects[[i]]$get_tabular_metrics(only_sliding = only_sliding, ases = ases) %>% 
           private$filter_best_caret_model(caret_compare_object = mlp_compare_objects[[i]])
         
-        # best_model_id = mlp_compare_objects[[i]]$get_best_model_id()
-        # 
-        # subset_results = subset_results %>% 
-        #   dplyr::filter(Model == best_model_id)
-        
         results = rbind(results, subset_results)
       }
         
       return(results)
     },
     
+    #' @description Computes the simple forecasts using all the models
+    #' @param lastn If TRUE, this will get the forecasts for the last n.ahead values of the realization (Default: FALSE). 
+    #'              If there is a ModelCompareNNforCaret object passed to this object, then lastn must be TRUE.
+    #' @param newxreg The future exogenous variable values to be used for prediction.
+    #'                Applicable to models that require the values of the new exogenous variables to be provided for future forecasts, e.g. nnfor::mlp()
+    #' @return The forecasted values
+    compute_simple_forecasts = function(lastn = FALSE, newxreg = NA){
+      
+      forecasts = data.frame()
+      
+      mlp_compare_objects = private$get_mlp_compare_objects()
+      if (length(mlp_compare_objects) >= 1 & lastn == TRUE){
+        stop(paste0("Your '", self$classname, "' object has a ModelCompareNNforCaret object which does not support plotting simple forecasts with lastn = TRUE. Please make lastn = FALSE and rerun with xreg passed."))
+      }
+      for (i in seq_along(mlp_compare_objects)){
+        subset_results = mlp_compare_objects[[i]]$plot_simple_forecasts(lastn = lastn, newxreg = newxreg, limits = FALSE, plot = FALSE)
+        
+        filtered = subset_results$forecasts %>%  
+          private$filter_best_caret_model(caret_compare_object = mlp_compare_objects[[i]])
+        
+        forecasts = dplyr::bind_rows(forecasts, filtered) 
+      }
+      
+      uni_compare_objects = private$get_uni_compare_objects()
+      for (i in seq_along(uni_compare_objects)){
+        subset_results = uni_compare_objects[[i]]$plot_simple_forecasts(lastn = lastn, newxreg = newxreg, limits = FALSE, plot = FALSE)
+        forecasts = dplyr::bind_rows(forecasts, subset_results$forecasts)
+      }
+      
+      var_compare_objects = private$get_var_compare_objects()
+      for (i in seq_along(var_compare_objects)){
+        subset_results = var_compare_objects[[i]]$plot_simple_forecasts(lastn = lastn, newxreg = newxreg, limits = FALSE, plot = FALSE)
+        forecasts = dplyr::bind_rows(forecasts, subset_results$forecasts)
+      }
+      
+      return(forecasts)
+    },
+    
     #' @description Creates an ensemble model based on all the models provided
     create_ensemble = function(){
+      data_for_model = self$get_tabular_metrics(only_sliding = TRUE, ases = FALSE) %>%
+        dplyr::distinct() %>%  # Remove duplicate entries for Model = 'Realization'
+        assertr::verify(assertr::has_all_names("Time", "Model", "f")) %>%
+        tidyr::pivot_wider(id_cols = Time, names_from = Model, values_from = f) %>% 
+        stats::na.omit() %>% 
+        dplyr::select(-Time)
+      
+      print(str(data_for_model))
+     
+      glm_ensemble = glm(formula = Realization ~ ., data = data_for_model)
+      
+      if (private$get_verbose() >= 1){
+        print(summary(glm_ensemble))
+      }
+      private$set_ensemble_model(model = glm_ensemble)
+    },
+    
+    #' @description Makes a prediction based on the ensemble model
+    #' @param naive If TRUE, the ensemble will be a simple mean of the prediction of all the models
+    #'              If FALSE, the ensemble will use a glm model created from the batch predictions of all the models
+    #' @param comb If 'naive' = TRUE, how to combine the predictions. Allowed values are 'mean' or 'median'
+    #' @param newxreg  The future exogenous variable values to be used for prediction.
+    #'                 Applicable to models that require the values of the new exogenous variables to be provided for future forecasts, e.g. nnfor::mlp()             
+    #' @return The predictions from each model along with the ensemble prediction                
+    predict_ensemble = function(naive = FALSE, comb = 'median', newxreg = NA){
+      
+      if (naive == TRUE){
+        if (comb != 'median' & comb != 'mean'){
+          warning(paste0("You are using a naive model, but the value of comb is set to '", comb, "' . The allowed values are 'median' or 'mean'. This will be set to the default value of 'median'."))
+          comb = 'median'
+        }
+      }
+      
+      forecasts = self$compute_simple_forecasts(lastn = FALSE, newxreg = newxreg) %>% 
+        assertr::verify(assertr::has_all_names("Time", "Model", "f")) %>% 
+        tidyr::pivot_wider(id_cols = Time, names_from = Model, values_from = f) %>% 
+        stats::na.omit() %>% 
+        dplyr::select(-Time)
+      
+      if (naive == TRUE){
+        if (comb == 'mean'){
+          forecasts = forecasts %>% 
+            dplyr::mutate(ensemble = rowMeans(.))
+        }
+        if (comb == 'median'){
+          forecasts = forecasts %>% 
+            dplyr::mutate(ensemble = Rfast::rowMedians(as.matrix(.)))
+        }
+      }
+      else{
+        forecasts = forecasts %>% 
+          dplyr::mutate(ensemble = stats::predict(private$get_ensemble_model(), newdata = forecasts)) %>% 
+          dplyr::mutate_if(is.numeric, as.double)  # Converts Named numeric (output of predict) to simple numeric 
+      }
+      
+      return(forecasts)
       
     }
-
   ),
   
   
@@ -258,6 +401,7 @@ ModelCombine = R6::R6Class(
     var_models = NA,
     mlp_models = NA,
     verbose = NA,
+    ensemble_model = NA,
     
     set_data = function(data){
       if (all(is.na(data))){ stop("You have not provided the time series data. Please provide to continue.") }
@@ -285,58 +429,12 @@ ModelCombine = R6::R6Class(
     },
     get_mlp_compare_objects = function(){return(private$mlp_models)},
     
-    get_len_x = function(){return(nrow(self$get_data()))},
-    
-    compute_simple_forecasts = function(lastn){
-      ## TODO: Needed for NNFOR
-      ## But add an argument xreg
-      ## Used by plot_simple_forecasts in the base class
-      
-      message("This function is not supported for nnfor::mlp at this time.")
-      
-      results = dplyr::tribble(~Model, ~Time, ~f, ~ll, ~ul)
-      # 
-      # if (lastn == FALSE){
-      #   data_start = 1
-      #   data_end = private$get_len_x()
-      #   train_data = self$get_data()[data_start:data_end, ]
-      #   
-      # }
-      # else{
-      #   data_start = 1
-      #   data_end = private$get_len_x() - self$get_n.ahead()
-      #   train_data = self$get_data()[data_start:data_end, ]
-      # }
-      # 
-      # from = data_end + 1
-      # to = data_end + self$get_n.ahead()
-      # 
-      # # Define Train Data
-      # 
-      # for (name in names(private$get_models())){
-      #   
-      #   var_interest = self$get_var_interest()
-      #   k = private$get_models()[[name]][['k_final']]
-      #   trend_type = private$get_models()[[name]][['trend_type']]
-      #   
-      #   # Fit model for the batch
-      #   varfit = vars::VAR(train_data, p=k, type=trend_type)
-      #   
-      #   # Forecast for the batch
-      #   forecasts = stats::predict(varfit, n.ahead=self$get_n.ahead())
-      #   forecasts = forecasts$fcst[[var_interest]] ## Get the forecasts only for the dependent variable
-      #   
-      #   results = results %>% 
-      #     dplyr::add_row(Model = name,
-      #                    Time = (from:to),
-      #                    f = forecasts[, 'fcst'],
-      #                    ll = forecasts[, 'lower'],
-      #                    ul = forecasts[, 'upper'])
-      #   
-      # }
-      # 
-      return(results)
+    set_ensemble_model = function(model){
+      private$ensemble_model = model
     },
+    get_ensemble_model = function(){return(private$ensemble_model)},
+    
+    get_len_x = function(){return(nrow(self$get_data()))},
     
     filter_best_caret_model = function(data, caret_compare_object){
       # Given a caret_compare_object and a dataframe 'data' that has a 'Model' column
diff --git a/R/ModelCompareBase.R b/R/ModelCompareBase.R
index f25ab5c..582d177 100644
--- a/R/ModelCompareBase.R
+++ b/R/ModelCompareBase.R
@@ -225,7 +225,8 @@ ModelCompareBase = R6::R6Class(
     #' @param zoom A number indicating how much to zoom into the plot. 
     #'             For example zoom = 50 will only plot the last 50 points of the realization
     #'             Useful for cases where realizations that are long and n.ahead is small.
-    plot_simple_forecasts = function(lastn = FALSE, newxreg = NA, limits = FALSE, zoom = NA){
+    #' @param plot If FALSE the plots are not plotted; useful when you want to just return the data (Default = TRUE)
+    plot_simple_forecasts = function(lastn = FALSE, newxreg = NA, limits = FALSE, zoom = NA, plot = TRUE){
       
       forecasts = private$compute_simple_forecasts_with_validation(lastn = lastn, newxreg = newxreg)
         
@@ -236,6 +237,7 @@ ModelCompareBase = R6::R6Class(
                        ll = self$get_data_var_interest(),
                        ul = self$get_data_var_interest())
       
+      
       if (!is.na(zoom)){
         zoom = private$validate_zoom(zoom)
         
@@ -246,20 +248,22 @@ ModelCompareBase = R6::R6Class(
           dplyr::filter(Time >= start)
       }
       
-      p = ggplot2::ggplot() +
-        ggplot2::geom_line(results %>% dplyr::filter(Model == "Actual"), mapping = ggplot2::aes(x=Time, y=f, color = Model), size = 1) +
-        ggplot2::geom_line(results %>% dplyr::filter(Model != "Actual"), mapping = ggplot2::aes(x=Time, y=f, color = Model), size = 0.75) +
-        ggplot2::ylab("Simple Forecasts")
-      
-      if (limits == TRUE){
-        p = p + 
-          ggplot2::geom_line(results, mapping = ggplot2::aes(x=Time, y=ll, color = Model), linetype = "dashed", size = 0.5) +
-          ggplot2::geom_line(results, mapping = ggplot2::aes(x=Time, y=ul, color = Model), linetype = "dashed", size = 0.5)
+      if (plot == TRUE){  
+        p = ggplot2::ggplot() +
+          ggplot2::geom_line(results %>% dplyr::filter(Model == "Actual"), mapping = ggplot2::aes(x=Time, y=f, color = Model), size = 1) +
+          ggplot2::geom_line(results %>% dplyr::filter(Model != "Actual"), mapping = ggplot2::aes(x=Time, y=f, color = Model), size = 0.75) +
+          ggplot2::ylab("Simple Forecasts")
+        
+        if (limits == TRUE){
+          p = p + 
+            ggplot2::geom_line(results, mapping = ggplot2::aes(x=Time, y=ll, color = Model), linetype = "dashed", size = 0.5) +
+            ggplot2::geom_line(results, mapping = ggplot2::aes(x=Time, y=ul, color = Model), linetype = "dashed", size = 0.5)
+        }
+        
+        print(p)
       }
       
-      print(p)
-      
-      return(forecasts)
+      return(list(forecasts = forecasts, plot_data = results))
       
     },
     
diff --git a/R/ModelCompareNNforCaret.R b/R/ModelCompareNNforCaret.R
index 6f54fdf..d431325 100644
--- a/R/ModelCompareNNforCaret.R
+++ b/R/ModelCompareNNforCaret.R
@@ -129,7 +129,7 @@ ModelCompareNNforCaret = R6::R6Class(
     
     compute_simple_forecasts = function(lastn, newxreg){
       if (lastn == TRUE){
-        message("This class does not support lastn = TRUE since the model has already been built using the entire data. Hence, lastn will be set to FALSE.")
+        message(paste0("The '",  self$classname, "' class does not support lastn = TRUE since the model has already been built using the entire data. Hence, lastn will be set to FALSE."))
         lastn = FALSE
       }
       
diff --git a/build/tswgewrapped_1.8.10.6.tar.gz b/build/tswgewrapped_1.8.10.6.tar.gz
new file mode 100644
index 0000000..322a900
Binary files /dev/null and b/build/tswgewrapped_1.8.10.6.tar.gz differ
diff --git a/inst/extdata/caret_model_train_bs120.rds b/inst/extdata/caret_model_train_bs120.rds
new file mode 100644
index 0000000..d23e040
Binary files /dev/null and b/inst/extdata/caret_model_train_bs120.rds differ
diff --git a/inst/extdata/caret_model_batch_ase_train.rds b/inst/extdata/caret_model_train_bs130.rds
similarity index 100%
rename from inst/extdata/caret_model_batch_ase_train.rds
rename to inst/extdata/caret_model_train_bs130.rds
diff --git a/inst/extdata/ensemble_glm_train_bs120.csv b/inst/extdata/ensemble_glm_train_bs120.csv
new file mode 100644
index 0000000..2631e64
--- /dev/null
+++ b/inst/extdata/ensemble_glm_train_bs120.csv
@@ -0,0 +1,3 @@
+"reps19_hd2_sdetFALSE","Univar A","Univar B","Univar C","AIC Both - R","AIC Trend - R","ensemble"
+8.25875556986836,8.2597570927,8.2642322988,8.2642322988,8.26087898347003,8.26020162823867,8.2643919071676
+8.26214895340459,8.26962681553,8.27937511312,8.27937511312,8.26549897076232,8.26297965565337,8.27499807022726
diff --git a/inst/extdata/ensemble_naive_mean_train_bs120.csv b/inst/extdata/ensemble_naive_mean_train_bs120.csv
new file mode 100644
index 0000000..901cbae
--- /dev/null
+++ b/inst/extdata/ensemble_naive_mean_train_bs120.csv
@@ -0,0 +1,3 @@
+"reps19_hd2_sdetFALSE","Univar A","Univar B","Univar C","AIC Both - R","AIC Trend - R","ensemble"
+8.25875556986836,8.2597570927,8.2642322988,8.2642322988,8.26087898347003,8.26020162823867,8.26134297864617
+8.26214895340459,8.26962681553,8.27937511312,8.27937511312,8.26549897076232,8.26297965565337,8.26983410359838
diff --git a/inst/extdata/ensemble_naive_median_train_bs120.csv b/inst/extdata/ensemble_naive_median_train_bs120.csv
new file mode 100644
index 0000000..c41d164
--- /dev/null
+++ b/inst/extdata/ensemble_naive_median_train_bs120.csv
@@ -0,0 +1,3 @@
+"reps19_hd2_sdetFALSE","Univar A","Univar B","Univar C","AIC Both - R","AIC Trend - R","ensemble"
+8.25875556986836,8.2597570927,8.2642322988,8.2642322988,8.26087898347003,8.26020162823867,8.26054030585435
+8.26214895340459,8.26962681553,8.27937511312,8.27937511312,8.26549897076232,8.26297965565337,8.26756289314616
diff --git a/inst/extdata/mdl_combine_ases1_train_bs120.csv b/inst/extdata/mdl_combine_ases1_train_bs120.csv
new file mode 100644
index 0000000..dae70fb
--- /dev/null
+++ b/inst/extdata/mdl_combine_ases1_train_bs120.csv
@@ -0,0 +1,42 @@
+"Model","ASE","Time_Test_Start","Time_Test_End","Batch"
+"Univar A",3.12308225764145e-05,119,120,1
+"Univar A",8.31434200888188e-05,121,122,2
+"Univar A",8.99107613814644e-05,123,124,3
+"Univar A",9.23963889730699e-05,125,126,4
+"Univar A",3.58922274584229e-05,127,128,5
+"Univar A",3.96032672843582e-05,129,130,6
+"Univar A",5.75283877703727e-05,131,132,7
+"Univar A",0.000179876397666713,133,134,8
+"Univar B",3.11894455528081e-05,119,120,1
+"Univar B",0.000195071292086915,121,122,2
+"Univar B",2.0555112379594e-05,123,124,3
+"Univar B",3.4537354499041e-06,125,126,4
+"Univar B",0.000238009765613329,127,128,5
+"Univar B",5.00922427881027e-05,129,130,6
+"Univar B",3.61710997771645e-06,131,132,7
+"Univar B",0.000133945482877433,133,134,8
+"Univar C",0.000133945482877433,133,134,1
+"AIC Both - R",9.53415765907678e-05,119,120,1
+"AIC Both - R",5.63432845915735e-05,121,122,2
+"AIC Both - R",0.000162816178046665,123,124,3
+"AIC Both - R",5.09735054355053e-06,125,126,4
+"AIC Both - R",0.000149402359459911,127,128,5
+"AIC Both - R",0.000352126894297292,129,130,6
+"AIC Both - R",0.00111179164980607,131,132,7
+"AIC Both - R",9.41956795816891e-05,133,134,8
+"AIC Trend - R",4.07323799036183e-06,119,120,1
+"AIC Trend - R",0.000225427731940963,121,122,2
+"AIC Trend - R",1.84818669129706e-05,123,124,3
+"AIC Trend - R",9.46361043687706e-06,125,126,4
+"AIC Trend - R",0.00019476432486588,127,128,5
+"AIC Trend - R",0.00032344234699412,129,130,6
+"AIC Trend - R",0.000689865028845393,131,132,7
+"AIC Trend - R",2.47277635958661e-06,133,134,8
+"reps19_hd2_sdetFALSE",4.01677184126864e-05,119,120,1
+"reps19_hd2_sdetFALSE",0.000272858996316274,121,122,2
+"reps19_hd2_sdetFALSE",0.000262383678444808,123,124,3
+"reps19_hd2_sdetFALSE",1.96260233679372e-05,125,126,4
+"reps19_hd2_sdetFALSE",5.52812013604825e-05,127,128,5
+"reps19_hd2_sdetFALSE",5.52334056835546e-05,129,130,6
+"reps19_hd2_sdetFALSE",0.000219281569444876,131,132,7
+"reps19_hd2_sdetFALSE",0.000640217368054598,133,134,8
diff --git a/inst/extdata/mdl_combine_ases1_train.csv b/inst/extdata/mdl_combine_ases1_train_bs130.csv
similarity index 100%
rename from inst/extdata/mdl_combine_ases1_train.csv
rename to inst/extdata/mdl_combine_ases1_train_bs130.csv
diff --git a/inst/extdata/mdl_combine_ases2_train_bs120.csv b/inst/extdata/mdl_combine_ases2_train_bs120.csv
new file mode 100644
index 0000000..64b35d9
--- /dev/null
+++ b/inst/extdata/mdl_combine_ases2_train_bs120.csv
@@ -0,0 +1,41 @@
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diff --git a/inst/extdata/mdl_combine_ases2_train.csv b/inst/extdata/mdl_combine_ases2_train_bs130.csv
similarity index 100%
rename from inst/extdata/mdl_combine_ases2_train.csv
rename to inst/extdata/mdl_combine_ases2_train_bs130.csv
diff --git a/inst/extdata/mdl_combine_forecasts1_train_bs120.csv b/inst/extdata/mdl_combine_forecasts1_train_bs120.csv
new file mode 100644
index 0000000..1cbdb03
--- /dev/null
+++ b/inst/extdata/mdl_combine_forecasts1_train_bs120.csv
@@ -0,0 +1,955 @@
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diff --git a/inst/extdata/mdl_combine_forecasts1_train.csv b/inst/extdata/mdl_combine_forecasts1_train_bs130.csv
similarity index 100%
rename from inst/extdata/mdl_combine_forecasts1_train.csv
rename to inst/extdata/mdl_combine_forecasts1_train_bs130.csv
diff --git a/inst/extdata/mdl_combine_forecasts2_train_bs120.csv b/inst/extdata/mdl_combine_forecasts2_train_bs120.csv
new file mode 100644
index 0000000..ecea8d4
--- /dev/null
+++ b/inst/extdata/mdl_combine_forecasts2_train_bs120.csv
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diff --git a/inst/extdata/mdl_combine_forecasts2_train.csv b/inst/extdata/mdl_combine_forecasts2_train_bs130.csv
similarity index 100%
rename from inst/extdata/mdl_combine_forecasts2_train.csv
rename to inst/extdata/mdl_combine_forecasts2_train_bs130.csv
diff --git a/man/ModelCombine.Rd b/man/ModelCombine.Rd
index f93f830..6decf8f 100644
--- a/man/ModelCombine.Rd
+++ b/man/ModelCombine.Rd
@@ -16,12 +16,15 @@ R6 class ModelCompareMultivariate
 \item \href{#method-get_var_interest}{\code{ModelCombine$get_var_interest()}}
 \item \href{#method-get_data_var_interest}{\code{ModelCombine$get_data_var_interest()}}
 \item \href{#method-set_verbose}{\code{ModelCombine$set_verbose()}}
+\item \href{#method-plot_simple_forecasts}{\code{ModelCombine$plot_simple_forecasts()}}
 \item \href{#method-plot_batch_forecasts}{\code{ModelCombine$plot_batch_forecasts()}}
 \item \href{#method-plot_batch_ases}{\code{ModelCombine$plot_batch_ases()}}
 \item \href{#method-plot_boxplot_ases}{\code{ModelCombine$plot_boxplot_ases()}}
 \item \href{#method-statistical_compare}{\code{ModelCombine$statistical_compare()}}
 \item \href{#method-get_tabular_metrics}{\code{ModelCombine$get_tabular_metrics()}}
+\item \href{#method-compute_simple_forecasts}{\code{ModelCombine$compute_simple_forecasts()}}
 \item \href{#method-create_ensemble}{\code{ModelCombine$create_ensemble()}}
+\item \href{#method-predict_ensemble}{\code{ModelCombine$predict_ensemble()}}
 \item \href{#method-clone}{\code{ModelCombine$clone()}}
 }
 }
@@ -121,6 +124,37 @@ Adjust the verbosity level
 }
 }
 \if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-plot_simple_forecasts"></a>}}
+\if{latex}{\out{\hypertarget{method-plot_simple_forecasts}{}}}
+\subsection{Method \code{plot_simple_forecasts()}}{
+Plots the simple forecast for each model
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{ModelCombine$plot_simple_forecasts(
+  lastn = FALSE,
+  newxreg = NA,
+  limits = FALSE,
+  zoom = NA
+)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{lastn}}{If TRUE, this will plot the forecasts forthe last n.ahead values of the realization (Default: FALSE)}
+
+\item{\code{newxreg}}{The future exogenous variable values to be used for prediction.
+Applicable to models that require the values of the new exogenous variables to be provided for future forecasts, e.g. nnfor::mlp()}
+
+\item{\code{limits}}{If TRUE, this will also plot the lower and upper limits of the forecasts (Default: FALSE)}
+
+\item{\code{zoom}}{A number indicating how much to zoom into the plot. 
+For example zoom = 50 will only plot the last 50 points of the realization
+Useful for cases where realizations that are long and n.ahead is small.}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
 \if{html}{\out{<a id="method-plot_batch_forecasts"></a>}}
 \if{latex}{\out{\hypertarget{method-plot_batch_forecasts}{}}}
 \subsection{Method \code{plot_batch_forecasts()}}{
@@ -202,6 +236,30 @@ forecasts, and the lower and upper limits asscoiated with the forecasts}
 }
 }
 \if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-compute_simple_forecasts"></a>}}
+\if{latex}{\out{\hypertarget{method-compute_simple_forecasts}{}}}
+\subsection{Method \code{compute_simple_forecasts()}}{
+Computes the simple forecasts using all the models
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{ModelCombine$compute_simple_forecasts(lastn = FALSE, newxreg = NA)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{lastn}}{If TRUE, this will get the forecasts for the last n.ahead values of the realization (Default: FALSE). 
+If there is a ModelCompareNNforCaret object passed to this object, then lastn must be TRUE.}
+
+\item{\code{newxreg}}{The future exogenous variable values to be used for prediction.
+Applicable to models that require the values of the new exogenous variables to be provided for future forecasts, e.g. nnfor::mlp()}
+}
+\if{html}{\out{</div>}}
+}
+\subsection{Returns}{
+The forecasted values
+}
+}
+\if{html}{\out{<hr>}}
 \if{html}{\out{<a id="method-create_ensemble"></a>}}
 \if{latex}{\out{\hypertarget{method-create_ensemble}{}}}
 \subsection{Method \code{create_ensemble()}}{
@@ -210,6 +268,32 @@ Creates an ensemble model based on all the models provided
 \if{html}{\out{<div class="r">}}\preformatted{ModelCombine$create_ensemble()}\if{html}{\out{</div>}}
 }
 
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-predict_ensemble"></a>}}
+\if{latex}{\out{\hypertarget{method-predict_ensemble}{}}}
+\subsection{Method \code{predict_ensemble()}}{
+Makes a prediction based on the ensemble model
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{ModelCombine$predict_ensemble(naive = FALSE, comb = "median", newxreg = NA)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{naive}}{If TRUE, the ensemble will be a simple mean of the prediction of all the models
+If FALSE, the ensemble will use a glm model created from the batch predictions of all the models}
+
+\item{\code{comb}}{If 'naive' = TRUE, how to combine the predictions. Allowed values are 'mean' or 'median'}
+
+\item{\code{newxreg}}{The future exogenous variable values to be used for prediction.
+Applicable to models that require the values of the new exogenous variables to be provided for future forecasts, e.g. nnfor::mlp()}
+}
+\if{html}{\out{</div>}}
+}
+\subsection{Returns}{
+The predictions from each model along with the ensemble prediction
+}
 }
 \if{html}{\out{<hr>}}
 \if{html}{\out{<a id="method-clone"></a>}}
diff --git a/man/ModelCompareBase.Rd b/man/ModelCompareBase.Rd
index 363a3c6..ceba02b 100644
--- a/man/ModelCompareBase.Rd
+++ b/man/ModelCompareBase.Rd
@@ -261,7 +261,8 @@ Plots the simple forecast for each model
   lastn = FALSE,
   newxreg = NA,
   limits = FALSE,
-  zoom = NA
+  zoom = NA,
+  plot = TRUE
 )}\if{html}{\out{</div>}}
 }
 
@@ -277,6 +278,8 @@ Plots the simple forecast for each model
 \item{\code{zoom}}{A number indicating how much to zoom into the plot. 
 For example zoom = 50 will only plot the last 50 points of the realization
 Useful for cases where realizations that are long and n.ahead is small.}
+
+\item{\code{plot}}{If FALSE the plots are not plotted; useful when you want to just return the data (Default = TRUE)}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/tests/testthat/test-BuildNNforCaret.R b/tests/testthat/test-BuildNNforCaret.R
index 015fb12..bad4896 100644
--- a/tests/testthat/test-BuildNNforCaret.R
+++ b/tests/testthat/test-BuildNNforCaret.R
@@ -11,7 +11,7 @@
 test_that("Random Serial", {
   # http://r-pkgs.had.co.nz/tests.html
   # TODO: Disable test before submitting to CRAN
-  # testthat::skip_on_cran()
+  testthat::skip_on_cran()
   
   # Load Data
   data = USeconomic
diff --git a/tests/testthat/test-ModelCombine.R b/tests/testthat/test-ModelCombine.R
index 9491137..cd5b042 100644
--- a/tests/testthat/test-ModelCombine.R
+++ b/tests/testthat/test-ModelCombine.R
@@ -6,7 +6,7 @@ test_that("Combine Models", {
   data = USeconomic
   
   var_interest = 'logGNP'
-  batch_size = 130
+  batch_size = 120
   n.ahead = 2
   
   data_train = data %>% dplyr::slice(1:(dplyr::n()-n.ahead))
@@ -69,9 +69,11 @@ test_that("Combine Models", {
   # 
   # model$summarize_hyperparam_results()
   # caret_model = model$get_final_models(subset = 'a')
-  # # saveRDS(caret_model, "caret_model_batch_ase_train.rds")
   
-  file = system.file("extdata", "caret_model_batch_ase_train.rds", package = "tswgewrapped", mustWork = TRUE)
+  file_type = "train"
+  file_name = paste0("caret_model_", file_type, "_bs", batch_size, ".rds")
+  # saveRDS(caret_model, file_name)
+  file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
   caret_model = readRDS(file)
   
   # Initialize the ModelCompareMultivariateVAR object
@@ -91,30 +93,39 @@ test_that("Combine Models", {
   
   ## Tabular Metrics ASEs
   ases1 = mdl_combine$get_tabular_metrics()
-  # write.csv(ases1, file = "mdl_combine_ases1_train.csv", row.names = FALSE)
-  ases1_file = system.file("extdata", "mdl_combine_ases1_train.csv", package = "tswgewrapped", mustWork = TRUE)
+  
+  file_type = "ases1"
+  file_name = paste0("mdl_combine_", file_type, "_train_bs", batch_size, ".csv")
+  # write.csv(ases1, file = file_name, row.names = FALSE)
+  ases1_file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
   ases1_target = read.csv(ases1_file, header = TRUE, stringsAsFactors = FALSE)
   good1 = all.equal(as.data.frame(ases1), ases1_target %>% dplyr::mutate_if(is.numeric, as.double))
   testthat::expect_equal(good1, TRUE)
   
   ases2 = mdl_combine$get_tabular_metrics(only_sliding = TRUE)
-  # write.csv(ases2, file = "mdl_combine_ases2_train.csv", row.names = FALSE)
-  ases2_file = system.file("extdata", "mdl_combine_ases2_train.csv", package = "tswgewrapped", mustWork = TRUE)
+  file_type = "ases2"
+  file_name = paste0("mdl_combine_", file_type, "_train_bs", batch_size, ".csv")
+  # write.csv(ases2, file = file_name, row.names = FALSE)
+  ases2_file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
   ases2_target = read.csv(ases2_file, header = TRUE, stringsAsFactors = FALSE)
   good2 = all.equal(as.data.frame(ases2), ases2_target %>% dplyr::mutate_if(is.numeric, as.double))
   testthat::expect_equal(good2, TRUE)
   
   ## Tabular Metrics Forecasts
   forecasts1 = mdl_combine$get_tabular_metrics(ases = FALSE)
-  # write.csv(forecasts1, file = "mdl_combine_forecasts1_train.csv", row.names = FALSE)
-  forecasts1_file = system.file("extdata", "mdl_combine_forecasts1_train.csv", package = "tswgewrapped", mustWork = TRUE)
+  file_type = "forecasts1"
+  file_name = paste0("mdl_combine_", file_type, "_train_bs", batch_size, ".csv")
+  # write.csv(forecasts1, file = file_name, row.names = FALSE)
+  forecasts1_file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
   forecasts1_target = read.csv(forecasts1_file, header = TRUE, stringsAsFactors = FALSE)
   good3 = all.equal(as.data.frame(forecasts1), forecasts1_target %>% dplyr::mutate_if(is.numeric, as.double))
   testthat::expect_equal(good3, TRUE)
   
   forecasts2 = mdl_combine$get_tabular_metrics(ases = FALSE, only_sliding = TRUE)
-  # write.csv(forecasts2, file = "mdl_combine_forecasts2_train.csv", row.names = FALSE)
-  forecasts2_file = system.file("extdata", "mdl_combine_forecasts2_train.csv", package = "tswgewrapped", mustWork = TRUE)
+  file_type = "forecasts2"
+  file_name = paste0("mdl_combine_", file_type, "_train_bs", batch_size, ".csv")
+  # write.csv(forecasts2, file = file_name, row.names = FALSE)
+  forecasts2_file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
   forecasts2_target = read.csv(forecasts2_file, header = TRUE, stringsAsFactors = FALSE)
   good4 = all.equal(as.data.frame(forecasts2), forecasts2_target %>% dplyr::mutate_if(is.numeric, as.double))
   testthat::expect_equal(good4, TRUE)
@@ -128,12 +139,48 @@ test_that("Combine Models", {
   
   ## Statistical Comparison
   comparison = mdl_combine$statistical_compare()
-  testthat::expect_equal(round(summary(comparison)[[1]][['Pr(>F)']][1],6), 0.372514)
+  testthat::expect_equal(round(summary(comparison)[[1]][['Pr(>F)']][1],6), 0.441591)
   
   ## Forecast Compariaon
-  data_test[var_interest]
-  mdl_compare_uni$plot_simple_forecasts(lastn = FALSE)
-  mdl_compare_var$plot_simple_forecasts(lastn = FALSE)
-  mdl_compare_mlp$plot_simple_forecasts(lastn = FALSE, newxreg = data_test %>% dplyr::select(-!!var_interest), zoom = 5) 
+  test_var_interest = data_test[var_interest]
+  newxreg = data_test %>% dplyr::select(-!!var_interest)
+  # # Individual
+  # p = mdl_compare_uni$plot_simple_forecasts(lastn = FALSE)
+  # p = mdl_compare_var$plot_simple_forecasts(lastn = FALSE)
+  # p = mdl_compare_mlp$plot_simple_forecasts(lastn = FALSE, newxreg = data_test %>% dplyr::select(-!!var_interest), zoom = 5) 
+  # Combined
+  p = mdl_combine$plot_simple_forecasts(lastn = FALSE, newxreg = data_test %>% dplyr::select(-!!var_interest), zoom = 5) 
+  
+  mdl_combine$create_ensemble()
+  print("Expected Values")
+  print(test_var_interest)
+  
+  ensemble1 = mdl_combine$predict_ensemble(naive = TRUE, newxreg = newxreg)
+  file_type = "naive_median"
+  file_name = paste0("ensemble_", file_type, "_train_bs", batch_size, ".csv")
+  # write.csv(ensemble1, file = file_name, row.names = FALSE)
+  ensemble1_file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
+  ensemble1_target = read.csv(ensemble1_file, header = TRUE, stringsAsFactors = FALSE, check.names = FALSE)
+  good5 = all.equal(as.data.frame(ensemble1), ensemble1_target %>% dplyr::mutate_if(is.numeric, as.double))
+  testthat::expect_equal(good5, TRUE)
+  
+  ensemble2 = mdl_combine$predict_ensemble(naive = TRUE, comb = 'mean', newxreg = newxreg)
+  file_type = "naive_mean"
+  file_name = paste0("ensemble_", file_type, "_train_bs", batch_size, ".csv")
+  # write.csv(ensemble2, file = file_name, row.names = FALSE)
+  ensemble2_file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
+  ensemble2_target = read.csv(ensemble2_file, header = TRUE, stringsAsFactors = FALSE, check.names = FALSE)
+  good6 = all.equal(as.data.frame(ensemble2), ensemble2_target %>% dplyr::mutate_if(is.numeric, as.double))
+  testthat::expect_equal(good6, TRUE)
+  
+  ensemble3 = mdl_combine$predict_ensemble(naive = FALSE, newxreg = newxreg)
+  file_type = "glm"
+  file_name = paste0("ensemble_", file_type, "_train_bs", batch_size, ".csv")
+  # write.csv(ensemble3, file = file_name, row.names = FALSE)
+  ensemble3_file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
+  ensemble3_target = read.csv(ensemble3_file, header = TRUE, stringsAsFactors = FALSE, check.names = FALSE)
+  good7 = all.equal(as.data.frame(ensemble3) %>% dplyr::mutate_if(is.numeric, as.double), ensemble3_target %>% dplyr::mutate_if(is.numeric, as.double))
+  testthat::expect_equal(good7, TRUE)
+  
 
 })
diff --git a/vignettes/ModelCombine.Rmd b/vignettes/ModelCombine.Rmd
index 16e0c70..a9c491e 100644
--- a/vignettes/ModelCombine.Rmd
+++ b/vignettes/ModelCombine.Rmd
@@ -17,11 +17,11 @@ knitr::opts_chunk$set(
 # Setup Libraries
 ```{r setup}
 library(tswgewrapped)
+library(dplyr)
 ```
 
 # Load Data
 ```{r}
-# Load Data
 file = system.file("extdata", "USeconomic.csv", package = "tswgewrapped", mustWork = TRUE)
 USeconomic = read.csv(file, header = TRUE, stringsAsFactors = FALSE, check.names = FALSE)
 names(USeconomic) = gsub("[(|)]", "", colnames(USeconomic))
@@ -31,10 +31,16 @@ data = USeconomic
 # Model Global Settings
 ```{r}
 var_interest = 'logGNP'
-batch_size = 132
+batch_size = 120
 n.ahead = 2
 ```
 
+# Train / Test Split
+```{r}
+data_train = data %>% dplyr::slice(1:(dplyr::n()-n.ahead))
+data_test = data %>% dplyr::slice((dplyr::n()-n.ahead), dplyr::n())
+```
+
 # 1.0 Build and Compare Models
 
 ## 1.1 Univariate
@@ -43,8 +49,10 @@ models = list("Univar A" = list(phi = 0.9, d = 1, s = 0, sliding_ase = TRUE),
               "Univar B" = list(phi = 0.9, d = 1, s = 4, sliding_ase = TRUE),
               "Univar C" = list(phi = 0.9, d = 1, s = 4, sliding_ase = FALSE)
               )
+```
 
-mdl_compare_uni = ModelCompareUnivariate$new(data = data, var_interest = var_interest, mdl_list = models,
+```{r}
+mdl_compare_uni = ModelCompareUnivariate$new(data = data_train, var_interest = var_interest, mdl_list = models,
                                              n.ahead = n.ahead, batch_size = batch_size)
 
 ```
@@ -52,27 +60,33 @@ mdl_compare_uni = ModelCompareUnivariate$new(data = data, var_interest = var_int
 ## 1.2 VAR
 ```{r}
 lag.max = 10
-
+  
 models = list("AIC None" = list(select = "aic", trend_type = "none", lag.max = lag.max),
               "AIC Trend" = list(select = "aic", trend_type = "trend", lag.max = lag.max),
               "AIC Both" = list(select = "aic", trend_type = "both", lag.max = lag.max))
+```
 
-
-mdl_build_var = ModelBuildMultivariateVAR$new(data = data, var_interest = var_interest,
+```{r}
+mdl_build_var = ModelBuildMultivariateVAR$new(data = data_train, var_interest = var_interest,
                                               mdl_list = models, verbose = 0)
+```
 
+```{r}
 mdl_build_var$build_recommended_models()
 models = mdl_build_var$get_final_models(subset = 'r')
+```
 
+```{r}
 # Setup Models to be compared with sliding ASE = TRUE
 for (name in names(models)){
   models[[name]][['sliding_ase']] = TRUE
 }
+```
 
+```{r}
 # Initialize the ModelCompareMultivariateVAR object
-mdl_compare_var = ModelCompareMultivariateVAR$new(data = data, var_interest = var_interest,
+mdl_compare_var = ModelCompareMultivariateVAR$new(data = data_train, var_interest = var_interest,
                                                   mdl_list = models, n.ahead = n.ahead, batch_size = batch_size, verbose = 0)
-
 ```
 
 ## 1.3 NNFOR::mlp() Caret ####
@@ -81,36 +95,45 @@ mdl_compare_var = ModelCompareMultivariateVAR$new(data = data, var_interest = va
 
 ```{r}
 # library(caret)
-# 
+
 # # Random Parallel
-# model = ModelBuildNNforCaret$new(data = data, var_interest = "logGNP", m = 2,
+# model = ModelBuildNNforCaret$new(data = data_train, var_interest = var_interest, m = 4,
 #                                  search = 'random',
 #                                  grid = NA, tuneLength = 2,
-#                                  batch_size = 132, h = 2,
+#                                  batch_size = batch_size, h = n.ahead,
 #                                  parallel = TRUE,
 #                                  seed = 1,
 #                                  verbose = 1)
 # 
 # model$summarize_hyperparam_results()
 # caret_model = model$get_final_models(subset = 'a')
+```
 
-file = system.file("extdata", "caret_model_batch_ase.rds", package = "tswgewrapped", mustWork = TRUE)
+```{r}
+file_type = "train"
+file_name = paste0("caret_model_", file_type, "_bs", batch_size, ".rds")
+file = system.file("extdata", file_name, package = "tswgewrapped", mustWork = TRUE)
 caret_model = readRDS(file)
+```
+
 
+```{r}
 # Initialize the ModelCompareMultivariateVAR object
-mdl_compare_mlp = ModelCompareNNforCaret$new(data = data, var_interest = var_interest,
+mdl_compare_mlp = ModelCompareNNforCaret$new(data = data_train, var_interest = var_interest,
                                              mdl_list = caret_model,
                                              verbose = 1)
+
 ```
 
 # 2.0 Combine all models
 
 ```{r}
-mdl_combine = ModelCombine$new(data = data, var_interest = "logGNP",
-                               uni_models = mdl_compare_uni, var_models = mdl_compare_var, mlp_models = mdl_compare_mlp,
-                               verbose = 1)
+mdl_combine = ModelCombine$new(data = data_train, var_interest = var_interest,
+                                 uni_models = mdl_compare_uni, var_models = mdl_compare_var, mlp_models = mdl_compare_mlp,
+                                 verbose = 1)
 ```
 
+
 ## Statistical Comparison
 ```{r}
 mdl_combine$plot_boxplot_ases()
@@ -131,6 +154,8 @@ mdl_combine$plot_batch_ases()
 ```
 
 ## Forecasts
+
+### Batch Forecasts
 ```{r}
 forecasts = mdl_combine$get_tabular_metrics(ases = FALSE)
 forecasts
@@ -140,3 +165,48 @@ forecasts
 mdl_combine$plot_batch_forecasts()
 ```
 
+### Simple Forecasts
+
+```{r}
+newxreg = data_test %>% dplyr::select(-!!var_interest)
+mdl_combine$compute_simple_forecasts(lastn = FALSE, newxreg = newxreg)
+```
+
+```{r}
+p = mdl_combine$plot_simple_forecasts(lastn = FALSE, newxreg = newxreg, zoom = 20) 
+```
+
+# Ensemble
+
+## Create the ensemble model(s)
+```{r}
+mdl_combine$create_ensemble()
+```
+ 
+## Forecasts with Ensemble Models
+
+```{r}
+test_var_interest = data_test[var_interest]
+print("Expected Values")
+print(test_var_interest)
+```
+
+### Naive with combine = 'median'
+```{r}
+ensemble1 = mdl_combine$predict_ensemble(naive = TRUE, comb = 'median', newxreg = newxreg)
+ensemble1
+```
+
+### Naive with combine = 'meean'
+```{r}
+ensemble2 = mdl_combine$predict_ensemble(naive = TRUE, comb = 'mean', newxreg = newxreg)
+ensemble2
+```
+
+### glm ensemble
+```{r}
+ensemble3 = mdl_combine$predict_ensemble(naive = FALSE, newxreg = newxreg)
+ensemble3
+```
+
+