#> Loading required package: modeltime
#> Finn Submission Info
#> • Experiment Name: finnts_fcst
-#> • Run Name: get_prepped_models-20240427T035416Z
+#> • Run Name: get_prepped_models-20240429T224931Z
#>
#> ℹ Prepping Data
-#> ✔ Prepping Data [951ms]
+#> ✔ Prepping Data [1s]
#>
#> ℹ Creating Model Workflows
-#> ✔ Creating Model Workflows [97ms]
+#> ✔ Creating Model Workflows [106ms]
#>
#> ℹ Creating Model Hyperparameters
-#> ✔ Creating Model Hyperparameters [139ms]
+#> ✔ Creating Model Hyperparameters [152ms]
#>
#> ℹ Creating Train Test Splits
-#> ✔ Creating Train Test Splits [342ms]
+#> ✔ Creating Train Test Splits [354ms]
#>
#> # A tibble: 31 × 4
#> Run_Type Train_Test_ID Train_End Test_End
diff --git a/articles/models-used-in-finnts.html b/articles/models-used-in-finnts.html
index 48af794f..f48ddcbe 100644
--- a/articles/models-used-in-finnts.html
+++ b/articles/models-used-in-finnts.html
@@ -158,7 +158,7 @@ Ensemble Models
Multistep Horizon Models
-By default within prep_models(), the multistep_horizon argument is set to TRUE. Meaning a multistep horizon approach is taken for specific multivariate models trained on the R1 feature engineering recipe. Below are the models that can run as multistep.
+By default within prep_models(), the multistep_horizon argument is set to FALSE. If set to TRUE, a multistep horizon approach is taken for specific multivariate models trained on the R1 feature engineering recipe. Below are the models that can run as multistep.
- cubist
- glmnet
@@ -167,7 +167,7 @@ Multistep Horizon Modelssvm-rbf
- xgboost
-A multistep model optimizes for each period in a forecast horizon. Let’s take an example of a monthly data set with a forecast horizon of 3. When creating the features for the R1 recipe, finnts will create lags of 1, 2, 3, 6, 9, 12 months. Then when training a mulitstep model it will iteratively use specific features to train the model. First it will train a model on the first forecast horizon (H1), where it will use all available feature lags. Then for H2 it will use lags of 2 or more. Finally for H3 it will use lags of 3 or more. So the final model is actually a collection of multiple models that each trained on a specific horizon. This let’s the model optimize for using all available data when creating the forecast. so in our example one glmnet model actually has three separate horizon specific models under the hood.
+A multistep model optimizes for each period in a forecast horizon. Let’s take an example of a monthly data set with a forecast horizon of 3. When creating the features for the R1 recipe, finnts will create lags of 1, 2, 3, 6, 9, 12 months. Then when training a mulitstep model it will iteratively use specific features to train the model. First it will train a model on the first forecast horizon (H1), where it will use all available feature lags. Then for H2 it will use lags of 2 or more. Finally for H3 it will use lags of 3 or more. So the final model is actually a collection of multiple models that each trained on a specific horizon. This lets the model optimize for using all available data when creating the forecast. So in our example, one glmnet model actually has three separate horizon specific models under the hood.
A few more things to mention. If multistep_horizon is TRUE then other multivariate models like arima-boost or prophet-xregs will not run a multistep horizon approach. Instead they will use lags that are equal to or greater than the forecast horizon. One set of hyperparameters will be chosen for each multistep model, meaning glmnet will only use one combination of final hyperparameters and apply it to each horizon model. Multistep models are not ran for the R2 recipe, since it has it’s own way of dealing with multiple horizons. Finally if feature_selection is turned on, it will be ran for each horizon specific model, meaning for a 3 month forecast horizon the feature selection process will be ran 3 times. One for each combination of features tied to a specific horizon.
diff --git a/favicon-16x16.png b/favicon-16x16.png
index 823a4243..9e9a6c59 100644
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diff --git a/favicon-32x32.png b/favicon-32x32.png
index c310822d..ab2f28b8 100644
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diff --git a/pkgdown.yml b/pkgdown.yml
index a2b6766b..bc370939 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -13,7 +13,7 @@ articles:
models-used-in-finnts: models-used-in-finnts.html
parallel-processing: parallel-processing.html
tips-for-production: tips-for-production.html
-last_built: 2024-04-27T03:52Z
+last_built: 2024-04-29T22:47Z
urls:
reference: https://microsoft.github.io/finnts/reference
article: https://microsoft.github.io/finnts/articles
diff --git a/reference/ensemble_models.html b/reference/ensemble_models.html
index 72bcb521..b9b91b7e 100644
--- a/reference/ensemble_models.html
+++ b/reference/ensemble_models.html
@@ -161,7 +161,7 @@ Examples
run_info <- set_run_info()
#> Finn Submission Info
#> • Experiment Name: finn_fcst
-#> • Run Name: finn_fcst-20240427T035210Z
+#> • Run Name: finn_fcst-20240429T224721Z
#>
prep_data(run_info,
@@ -172,7 +172,7 @@ Examples
forecast_horizon = 3
)
#> ℹ Prepping Data
-#> ✔ Prepping Data [1.4s]
+#> ✔ Prepping Data [1.5s]
#>
prep_models(run_info,
@@ -180,13 +180,13 @@ Examples
num_hyperparameters = 2
)
#> ℹ Creating Model Workflows
-#> ✔ Creating Model Workflows [180ms]
+#> ✔ Creating Model Workflows [178ms]
#>
#> ℹ Creating Model Hyperparameters
-#> ✔ Creating Model Hyperparameters [185ms]
+#> ✔ Creating Model Hyperparameters [186ms]
#>
#> ℹ Creating Train Test Splits
-#> ✔ Creating Train Test Splits [279ms]
+#> ✔ Creating Train Test Splits [284ms]
#>
train_models(run_info,
@@ -196,23 +196,35 @@ Examples
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
+#> There were issues with some computations A: x1
#> → B | warning: A correlation computation is required, but `estimate` is constant and has 0
#> standard deviation, resulting in a divide by 0 error. `NA` will be returned.
+#> There were issues with some computations A: x1
+#> There were issues with some computations A: x3 B: x1
+#>
#> ℹ Training Individual Models
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
+#> There were issues with some computations A: x2
+#> There were issues with some computations A: x3
+#>
+#> ℹ Training Individual Models
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
-#> ✔ Training Individual Models [9.4s]
+#> There were issues with some computations A: x1
+#> There were issues with some computations A: x3
+#>
+#> ℹ Training Individual Models
+#> ✔ Training Individual Models [10.2s]
#>
ensemble_models(run_info)
#> ℹ Training Ensemble Models
#> ℹ Not all time series were completed within 'ensemble_models', expected 1 time series but only 0 time series were ran. Some ran into errors.
#> ℹ Training Ensemble Models
-#> ✔ Training Ensemble Models [116ms]
+#> ✔ Training Ensemble Models [119ms]
#>
# }
diff --git a/reference/final_models.html b/reference/final_models.html
index 67ba6ffc..9a46b4e3 100644
--- a/reference/final_models.html
+++ b/reference/final_models.html
@@ -174,7 +174,7 @@ Examples
run_info <- set_run_info()
#> Finn Submission Info
#> • Experiment Name: finn_fcst
-#> • Run Name: finn_fcst-20240427T035222Z
+#> • Run Name: finn_fcst-20240429T224734Z
#>
prep_data(run_info,
@@ -185,7 +185,7 @@ Examples
forecast_horizon = 3
)
#> ℹ Prepping Data
-#> ✔ Prepping Data [3.2s]
+#> ✔ Prepping Data [3.3s]
#>
prep_models(run_info,
@@ -193,15 +193,15 @@ Examples
back_test_scenarios = 3
)
#> ℹ Creating Model Workflows
-#> ✔ Creating Model Workflows [126ms]
+#> ✔ Creating Model Workflows [120ms]
#>
#> ℹ Creating Model Hyperparameters
-#> ✔ Creating Model Hyperparameters [110ms]
+#> ✔ Creating Model Hyperparameters [105ms]
#>
#> ℹ Creating Train Test Splits
#> ℹ Turning ensemble models off since no multivariate models were chosen to run.
#> ℹ Creating Train Test Splits
-#> ✔ Creating Train Test Splits [263ms]
+#> ✔ Creating Train Test Splits [261ms]
#>
train_models(run_info,
@@ -214,29 +214,37 @@ Examples
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
+#> There were issues with some computations A: x1
#> → B | warning: A correlation computation is required, but `estimate` is constant and has 0
#> standard deviation, resulting in a divide by 0 error. `NA` will be returned.
-#> ℹ Training Individual Models
-#> There were issues with some computations A: x3 B: x2
+#> There were issues with some computations A: x1
#> There were issues with some computations A: x3 B: x2
#>
#> ℹ Training Individual Models
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
+#> There were issues with some computations A: x1
+#> There were issues with some computations A: x3
+#>
+#> ℹ Training Individual Models
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
-#> There were issues with some computations A: x1
+#> There were issues with some computations A: x2
#> → B | warning: A correlation computation is required, but `estimate` is constant and has 0
#> standard deviation, resulting in a divide by 0 error. `NA` will be returned.
-#> There were issues with some computations A: x1
+#> There were issues with some computations A: x2
#> There were issues with some computations A: x3 B: x2
#>
#> ℹ Training Individual Models
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
+#> There were issues with some computations A: x3
+#> There were issues with some computations A: x3
+#>
+#> ℹ Training Individual Models
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
@@ -250,7 +258,7 @@ Examples
#> → A | warning: A correlation computation is required, but the inputs are size zero or one and
#> the standard deviation cannot be computed. `NA` will be returned.
#> ℹ Training Individual Models
-#> There were issues with some computations A: x1
+#> There were issues with some computations A: x3
#> There were issues with some computations A: x3
#>
#> ℹ Training Individual Models
@@ -261,12 +269,12 @@ Examples
#> There were issues with some computations A: x3
#>
#> ℹ Training Individual Models
-#> ✔ Training Individual Models [19.9s]
+#> ✔ Training Individual Models [20.3s]
#>
final_models(run_info)
#> ℹ Selecting Best Models
-#> ✔ Selecting Best Models [444ms]
+#> ✔ Selecting Best Models [452ms]
#>
# }