diff --git a/articles/modeltime-spark.html b/articles/modeltime-spark.html index d320fbe7..e1e89d89 100644 --- a/articles/modeltime-spark.html +++ b/articles/modeltime-spark.html @@ -341,9 +341,9 @@

Nested Forecasting with Spark) #> Using existing parallel backend with 4 clusters (cores)... #> Beginning Parallel Loop | 0.003 seconds -#> Finishing parallel backend. Clusters are remaining open. | 19.625 seconds +#> Finishing parallel backend. Clusters are remaining open. | 21.105 seconds #> Close clusters by running: `parallel_stop()`. -#> Finished in: 19.62566 secs. +#> Finished in: 21.1058 secs.

The nested modeltime object has now fit the models using Spark.

 nested_modeltime_tbl
diff --git a/articles/nested-forecasting.html b/articles/nested-forecasting.html
index fdf565dc..a9e52ae5 100644
--- a/articles/nested-forecasting.html
+++ b/articles/nested-forecasting.html
@@ -411,7 +411,7 @@ 
Step 2: Nested Modeltime Tables  wflw_xgb
 )
 #> Fitting models on training data... ■■■■■                             14% | ETA:…
-#> Fitting models on training data... ■■■■■■■■■■■■■■■■■■■■■■            71% | ETA:…
+#> Fitting models on training data... ■■■■■■■■■■■■■■■■■■                57% | ETA:…
 #> Fitting models on training data... ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:…
 
 nested_modeltime_tbl
@@ -1215,7 +1215,7 @@ 
Step 5: Refitting and Future Forec
 #> ℹ [7/7] Starting Modeltime Table: ID 1_95...
 #> ✔ Model 2 Passed XGBOOST.
 #> ✔ [7/7] Finished Modeltime Table: ID 1_95
-#> Finished in: 1.932722 secs.
+#> Finished in: 2.038957 secs.

Note that we used control_nested_refit(verbose = TRUE) to display the modeling results as each model is refit. This is not necessary, but can be useful to follow the nested model fitting diff --git a/articles/parallel-processing.html b/articles/parallel-processing.html index e233422b..8497b278 100644 --- a/articles/parallel-processing.html +++ b/articles/parallel-processing.html @@ -499,9 +499,9 @@

Fitting Using Parallel Backend) #> Using existing parallel backend with 2 clusters (cores)... #> Beginning Parallel Loop | 0.006 seconds -#> Finishing parallel backend. Clusters are remaining open. | 8.601 seconds +#> Finishing parallel backend. Clusters are remaining open. | 8.691 seconds #> Close clusters by running: `parallel_stop()`. -#> Total time | 8.602 seconds +#> Total time | 8.692 seconds

This returns a modeltime table.

 +#> Total time | 14.155 seconds
diff --git a/pkgdown.yml b/pkgdown.yml index 0657fe13..b045934e 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -11,7 +11,7 @@ articles: nested-forecasting: nested-forecasting.html parallel-processing: parallel-processing.html recursive-forecasting: recursive-forecasting.html -last_built: 2024-07-29T15:26Z +last_built: 2024-07-29T17:02Z urls: reference: https://business-science.github.io/modeltime/reference article: https://business-science.github.io/modeltime/articles diff --git a/reference/adam_reg.html b/reference/adam_reg.html index bfa16f67..1fe9ad39 100644 --- a/reference/adam_reg.html +++ b/reference/adam_reg.html @@ -372,7 +372,7 @@

Examplesmodel_fit #> parsnip model object #> -#> Time elapsed: 0.73 seconds +#> Time elapsed: 0.7 seconds #> Model estimated using auto.adam() function: ETS(ANN) #> Distribution assumed in the model: S #> Loss function type: likelihood; Loss function value: -466.753 @@ -408,7 +408,7 @@

Examplesmodel_fit #> parsnip model object #> -#> Time elapsed: 0.84 seconds +#> Time elapsed: 1.05 seconds #> Model estimated using adam() function: ETS(AAdN)+ARIMA(3,1,3) #> Distribution assumed in the model: Normal #> Loss function type: likelihood; Loss function value: -442.9176 diff --git a/reference/add_modeltime_model.html b/reference/add_modeltime_model.html index cf7d8afc..893997c7 100644 --- a/reference/add_modeltime_model.html +++ b/reference/add_modeltime_model.html @@ -142,7 +142,7 @@

Examples#> ✖ smooth::pls() masks parsnip::pls() #> ✖ tidyr::spread() masks greybox::spread() #> ✖ recipes::step() masks stats::step() -#> • Dig deeper into tidy modeling with R at https://www.tmwr.org +#> • Search for functions across packages at https://www.tidymodels.org/find/ model_fit_ets <- exp_smoothing() %>% set_engine("ets") %>% diff --git a/reference/exp_smoothing.html b/reference/exp_smoothing.html index b8260a5e..ffc927e4 100644 --- a/reference/exp_smoothing.html +++ b/reference/exp_smoothing.html @@ -441,7 +441,7 @@

Examplesmodel_fit #> parsnip model object #> -#> Time elapsed: 0.4 seconds +#> Time elapsed: 0.12 seconds #> Model estimated using es() function: ETS(MAdA) #> Distribution assumed in the model: Normal #> Loss function type: likelihood; Loss function value: 1572.688 diff --git a/reference/m750.html b/reference/m750.html index 3363539d..b12649bd 100644 --- a/reference/m750.html +++ b/reference/m750.html @@ -103,7 +103,7 @@

Format<

Source

-

  • M4 Competition Website

    • +

    • M4 Competition Website: https://www.unic.ac.cy/iff/research/forecasting/m-competitions/m4/

diff --git a/reference/plot_modeltime_forecast.html b/reference/plot_modeltime_forecast.html index 04d18de6..f4cb6789 100644 --- a/reference/plot_modeltime_forecast.html +++ b/reference/plot_modeltime_forecast.html @@ -1,6 +1,6 @@ -Interactive Forecast Visualization — plot_modeltime_forecast • modeltimeInteractive Forecast Visualization — plot_modeltime_forecast • modeltime