Submodels Resample
all_model_fit_refit %>%
filter(.model_desc != "DBARTS")
## # Modeltime Table
## # A tibble: 10 × 5
## .model_id .model .model_desc .type .calibration_data
## <int> <list> <chr> <chr> <list>
## 1 1 <fit[+]> PROPHET Test <tibble [744 × 4]>
## 2 2 <workflow> GLMNET Test <tibble [744 × 4]>
## 3 3 <workflow> KERNLAB Test <tibble [744 × 4]>
## 4 4 <workflow> KERNLAB Test <tibble [744 × 4]>
## 5 5 <workflow> KKNN Test <tibble [744 × 4]>
## 6 6 <workflow> RANDOMFOREST Test <tibble [744 × 4]>
## 7 7 <workflow> XGBOOST Test <tibble [744 × 4]>
## 8 8 <workflow> CUBIST Test <tibble [744 × 4]>
## 9 9 <workflow> NNAR(2,1,10)[5] Test <tibble [744 × 4]>
## 10 10 <workflow> PROPHET W/ XGBOOST ERRORS Test <tibble [744 × 4]>
submodels_resample <- all_model_fit_refit %>%
filter(.model_desc != "DBARTS") %>%
modeltime_fit_resamples(
resamples = resamples_tscv_no_acum,
control = control_resamples(
verbose = TRUE,
# allow_par = FALSE,
allow_par = TRUE,
pkgs = c("Cubist", "rules", "tidymodels", "ranger", "parsnip", "modeltime")
)
)
## ── Fitting Resamples ────────────────────────────────────────────
## • Model ID: 1 PROPHET
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## • Model ID: 2 GLMNET
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## • Model ID: 3 KERNLAB
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## • Model ID: 4 KERNLAB
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## • Model ID: 5 KKNN
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## • Model ID: 6 RANDOMFOREST
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## • Model ID: 7 XGBOOST
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## • Model ID: 8 CUBIST
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## • Model ID: 9 NNAR(2,1,10)[5]
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## • Model ID: 10 PROPHET W/ XGBOOST ERRORS
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## 33.83 sec elapsed
submodels_resample %>% write_rds("submodels_resample")
submodels_resample$.resample_results[[1]]$.predictions
## [[1]]
## # A tibble: 64 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 7.16 2172 7.07 Preprocessor1_Model1
## 2 7.18 2173 7.04 Preprocessor1_Model1
## 3 7.20 2174 7.06 Preprocessor1_Model1
## 4 7.26 2175 7.07 Preprocessor1_Model1
## 5 7.29 2176 7.05 Preprocessor1_Model1
## 6 7.29 2177 6.99 Preprocessor1_Model1
## 7 7.32 2178 6.91 Preprocessor1_Model1
## 8 7.34 2179 6.87 Preprocessor1_Model1
## 9 7.41 2180 6.87 Preprocessor1_Model1
## 10 7.44 2181 6.85 Preprocessor1_Model1
## # ℹ 54 more rows
##
## [[2]]
## # A tibble: 64 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 7.29 1920 7.10 Preprocessor1_Model1
## 2 7.29 1921 7.12 Preprocessor1_Model1
## 3 7.29 1922 7.14 Preprocessor1_Model1
## 4 7.32 1923 7.14 Preprocessor1_Model1
## 5 7.38 1924 7.18 Preprocessor1_Model1
## 6 7.42 1925 7.26 Preprocessor1_Model1
## 7 7.42 1926 7.27 Preprocessor1_Model1
## 8 7.42 1927 7.19 Preprocessor1_Model1
## 9 7.44 1928 7.22 Preprocessor1_Model1
## 10 7.47 1929 7.26 Preprocessor1_Model1
## # ℹ 54 more rows
##
## [[3]]
## # A tibble: 64 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 7.38 1668 7.52 Preprocessor1_Model1
## 2 7.32 1669 7.51 Preprocessor1_Model1
## 3 7.24 1670 7.55 Preprocessor1_Model1
## 4 7.02 1671 7.55 Preprocessor1_Model1
## 5 6.96 1672 7.59 Preprocessor1_Model1
## 6 6.89 1673 7.73 Preprocessor1_Model1
## 7 6.82 1674 7.82 Preprocessor1_Model1
## 8 6.74 1675 7.88 Preprocessor1_Model1
## 9 6.56 1676 7.79 Preprocessor1_Model1
## 10 6.53 1677 7.81 Preprocessor1_Model1
## # ℹ 54 more rows
##
## [[4]]
## # A tibble: 64 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 6.99 1416 6.93 Preprocessor1_Model1
## 2 6.97 1417 6.96 Preprocessor1_Model1
## 3 6.96 1418 7.00 Preprocessor1_Model1
## 4 6.96 1419 6.98 Preprocessor1_Model1
## 5 6.96 1420 7.00 Preprocessor1_Model1
## 6 6.92 1421 6.95 Preprocessor1_Model1
## 7 6.89 1422 6.95 Preprocessor1_Model1
## 8 6.87 1423 6.92 Preprocessor1_Model1
## 9 6.86 1424 6.91 Preprocessor1_Model1
## 10 6.86 1425 6.90 Preprocessor1_Model1
## # ℹ 54 more rows
##
## [[5]]
## # A tibble: 64 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 7.04 1164 6.96 Preprocessor1_Model1
## 2 7.06 1165 6.87 Preprocessor1_Model1
## 3 7.05 1166 6.97 Preprocessor1_Model1
## 4 7.06 1167 6.86 Preprocessor1_Model1
## 5 7.07 1168 6.88 Preprocessor1_Model1
## 6 7.14 1169 6.93 Preprocessor1_Model1
## 7 7.17 1170 6.94 Preprocessor1_Model1
## 8 7.17 1171 6.91 Preprocessor1_Model1
## 9 7.18 1172 6.97 Preprocessor1_Model1
## 10 7.19 1173 6.97 Preprocessor1_Model1
## # ℹ 54 more rows
summary(submodels_resample)
## .model_id .model.Length .model.Class .model.Mode
## Min. : 1.00 6 _prophet_fit_impl list
## 1st Qu.: 3.25 4 workflow list
## Median : 5.50 4 workflow list
## Mean : 5.50 4 workflow list
## 3rd Qu.: 7.75 4 workflow list
## Max. :10.00 4 workflow list
## 4 workflow list
## 4 workflow list
## 4 workflow list
## 4 workflow list
## .model_desc .type
## Length:10 Length:10
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
##
##
##
## .calibration_data.Length .calibration_data.Class .calibration_data.Mode
## 4 tbl_df list
## 4 tbl_df list
## 4 tbl_df list
## 4 tbl_df list
## 4 tbl_df list
## 4 tbl_df list
## 4 tbl_df list
## 4 tbl_df list
## 4 tbl_df list
## 4 tbl_df list
## .resample_results.Length .resample_results.Class .resample_results.Mode
## 5 resample_results list
## 5 resample_results list
## 5 resample_results list
## 5 resample_results list
## 5 resample_results list
## 5 resample_results list
## 5 resample_results list
## 5 resample_results list
## 5 resample_results list
## 5 resample_results list
str(submodels_resample)
## mdl_tm_t [10 × 6] (S3: mdl_time_tbl/tbl_df/tbl/data.frame)
## $ .model_id : int [1:10] 1 2 3 4 5 6 7 8 9 10
## $ .model :List of 10
## ..$ :List of 6
## .. ..$ lvl : NULL
## .. ..$ spec :List of 8
## .. .. ..$ args :List of 12
## .. .. .. ..$ growth : language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ changepoint_num : language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ changepoint_range : language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ seasonality_yearly : language ~TRUE
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ seasonality_weekly : language ~TRUE
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ seasonality_daily : language ~TRUE
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ season : language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ prior_scale_changepoints: language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ prior_scale_seasonality : language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ prior_scale_holidays : language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ logistic_cap : language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. ..$ logistic_floor : language ~NULL
## .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. ..$ eng_args : Named list()
## .. .. .. ..- attr(*, "class")= chr [1:2] "quosures" "list"
## .. .. ..$ mode : chr "regression"
## .. .. ..$ user_specified_mode : logi TRUE
## .. .. ..$ method :List of 3
## .. .. .. ..$ libs: chr [1:2] "prophet" "modeltime"
## .. .. .. ..$ fit :List of 5
## .. .. .. .. ..$ interface: chr "data.frame"
## .. .. .. .. ..$ protect : chr [1:2] "x" "y"
## .. .. .. .. ..$ func : Named chr "prophet_fit_impl"
## .. .. .. .. .. ..- attr(*, "names")= chr "fun"
## .. .. .. .. ..$ defaults :List of 1
## .. .. .. .. .. ..$ uncertainty.samples: num 0
## .. .. .. .. ..$ args :List of 6
## .. .. .. .. .. ..$ x : language missing_arg()
## .. .. .. .. .. ..$ y : language missing_arg()
## .. .. .. .. .. ..$ yearly.seasonality : language ~TRUE
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ weekly.seasonality : language ~TRUE
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ daily.seasonality : language ~TRUE
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ uncertainty.samples: num 0
## .. .. .. ..$ pred:List of 1
## .. .. .. .. ..$ numeric:List of 4
## .. .. .. .. .. ..$ pre : NULL
## .. .. .. .. .. ..$ post: NULL
## .. .. .. .. .. ..$ func: Named chr "predict"
## .. .. .. .. .. .. ..- attr(*, "names")= chr "fun"
## .. .. .. .. .. ..$ args:List of 2
## .. .. .. .. .. .. ..$ object : language object$fit
## .. .. .. .. .. .. ..$ new_data: symbol new_data
## .. .. ..$ engine : chr "prophet"
## .. .. ..$ user_specified_engine: logi TRUE
## .. .. ..$ defaults :List of 1
## .. .. .. ..$ uncertainty.samples: num 0
## .. .. ..- attr(*, "class")= chr [1:2] "prophet_reg" "model_spec"
## .. ..$ fit :List of 4
## .. .. ..$ models:List of 1
## .. .. .. ..$ model_1:List of 32
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## .. .. .. .. ..$ changepoints : POSIXct[1:25], format: "2012-04-16" ...
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## .. .. .. .. ..$ time : num [1:2979] 15307 15308 15309 15310 15313 ...
## .. .. .. .. ..$ time_index.num: num [1:2979] 1322524800 1322611200 1322697600 1322784000 1323043200 ...
## .. .. .. .. ..$ time_year : int [1:2979] 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
## .. .. .. .. ..$ time_half : int [1:2979] 2 2 2 2 2 2 2 2 2 2 ...
## .. .. .. .. ..$ time_quarter : int [1:2979] 4 4 4 4 4 4 4 4 4 4 ...
## .. .. .. .. ..$ time_month : int [1:2979] 11 11 12 12 12 12 12 12 12 12 ...
## .. .. .. .. ..$ time_day : int [1:2979] 29 30 1 2 5 6 7 8 9 12 ...
## .. .. .. .. ..$ time_wday : int [1:2979] 3 4 5 6 2 3 4 5 6 2 ...
## .. .. .. .. ..$ time_mday : int [1:2979] 29 30 1 2 5 6 7 8 9 12 ...
## .. .. .. .. ..$ time_qday : int [1:2979] 60 61 62 63 66 67 68 69 70 73 ...
## .. .. .. .. ..$ time_yday : int [1:2979] 333 334 335 336 339 340 341 342 343 346 ...
## .. .. .. .. ..$ time_mweek : int [1:2979] 5 5 1 1 2 2 2 2 2 3 ...
## .. .. .. .. ..$ time_week : int [1:2979] 48 48 48 48 49 49 49 49 49 50 ...
## .. .. .. .. ..$ time_week2 : int [1:2979] 0 0 0 0 1 1 1 1 1 0 ...
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## .. .. .. .. ..$ time_week4 : int [1:2979] 0 0 0 0 1 1 1 1 1 2 ...
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## .. .. .. .. ..$ close: num [1:2979] 6.88 6.88 6.41 6.16 6.16 ...
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## .. .. .. .. ..$ time_quarter : int [1:2979] 4 4 4 4 4 4 4 4 4 4 ...
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## .. .. .. .. ..$ time_day : int [1:2979] 29 30 1 2 5 6 7 8 9 12 ...
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## .. .. .. .. ..$ time_week : int [1:2979] 48 48 48 48 49 49 49 49 49 50 ...
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## .. .. .. .. ..$ time_week3 : int [1:2979] 0 0 0 0 1 1 1 1 1 2 ...
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## .. .. .. .. ..$ time_mday7 : int [1:2979] 5 5 1 1 1 1 2 2 2 2 ...
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## .. .. .. .. .. .. .. .. ..$ neighbors: language rules::get_neighbors(object$spec$args)
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## .. .. .. .. .. .. .. .. ..$ neighbors: language rules::get_neighbors(object$spec$args)
## .. .. .. .. ..$ engine : chr "Cubist"
## .. .. .. .. ..$ user_specified_engine: logi TRUE
## .. .. .. .. ..- attr(*, "class")= chr [1:2] "cubist_rules" "model_spec"
## .. .. .. ..$ fit :List of 15
## .. .. .. .. ..$ data : chr "6\\.88199997,15307,1322524800,2011,2,4,11,29,3,29,60,333,5,48,0,0,0,5\n6\\.88199997,15308,1322611200,2011,2,4,1"| __truncated__
## .. .. .. .. ..$ names : chr "| Generated using R version 4.2.1 (2022-06-23 ucrt)\n| on Sat Nov 25 10:24:26 2023\noutcome.\n\noutcome: contin"| __truncated__
## .. .. .. .. ..$ caseWeights : logi FALSE
## .. .. .. .. ..$ model : chr "id=\"Cubist 2.07 GPL Edition 2023-11-25\"\nprec=\"4\" globalmean=\"7.143603\" extrap=\"1\" insts=\"0\" ceiling="| __truncated__
## .. .. .. .. ..$ output : chr "\nCubist [Release 2.07 GPL Edition] Sat Nov 25 10:24:26 2023\n---------------------------------\n\n Target "| __truncated__
## .. .. .. .. ..$ control :List of 6
## .. .. .. .. .. ..$ unbiased : logi FALSE
## .. .. .. .. .. ..$ rules : num 1000
## .. .. .. .. .. ..$ extrapolation: num 1
## .. .. .. .. .. ..$ sample : num 0
## .. .. .. .. .. ..$ label : chr "outcome"
## .. .. .. .. .. ..$ seed : int 4084
## .. .. .. .. ..$ committees : num 100
## .. .. .. .. ..$ maxd : num 3.8
## .. .. .. .. ..$ dims : int [1:2] 2979 17
## .. .. .. .. ..$ splits :'data.frame': 21812 obs. of 8 variables:
## .. .. .. .. .. ..$ committee : num [1:21812] 1 1 1 1 1 1 1 1 1 1 ...
## .. .. .. .. .. ..$ rule : num [1:21812] 1 1 2 2 2 3 3 4 4 4 ...
## .. .. .. .. .. ..$ variable : chr [1:21812] "time" "time" "time" "time_yday" ...
## .. .. .. .. .. ..$ dir : chr [1:21812] "<=" ">" "<=" "<=" ...
## .. .. .. .. .. ..$ value : num [1:21812] 15393 15376 15876 103 15471 ...
## .. .. .. .. .. ..$ category : chr [1:21812] "" "" "" "" ...
## .. .. .. .. .. ..$ type : chr [1:21812] "type2" "type2" "type2" "type2" ...
## .. .. .. .. .. ..$ percentile: num [1:21812] 0.0205 0.0161 0.1292 0.2853 0.0386 ...
## .. .. .. .. ..$ usage :'data.frame': 17 obs. of 3 variables:
## .. .. .. .. .. ..$ Conditions: num [1:17] 100 36 19 9 5 5 1 1 0 0 ...
## .. .. .. .. .. ..$ Model : num [1:17] 96 76 50 57 59 70 11 31 16 8 ...
## .. .. .. .. .. ..$ Variable : chr [1:17] "time" "time_yday" "time_qday" "time_week" ...
## .. .. .. .. ..$ call : language cubist.default(x = x, y = y, committees = 100, control = Cubist::cubistControl(rules = 1000))
## .. .. .. .. ..$ coefficients:'data.frame': 7655 obs. of 20 variables:
## .. .. .. .. .. ..$ (Intercept) : num [1:7655] -175.23 -7.57 353.34 144.95 43.15 ...
## .. .. .. .. .. ..$ time : num [1:7655] 0.011726 0.000841 -0.022623 -0.008907 -0.002398 ...
## .. .. .. .. .. ..$ time_index.num: num [1:7655] NA NA NA NA NA NA NA NA NA NA ...
## .. .. .. .. .. ..$ time_year : num [1:7655] NA NA NA NA NA NA NA NA NA NA ...
## .. .. .. .. .. ..$ time_half : num [1:7655] NA NA NA NA NA NA NA NA NA NA ...
## .. .. .. .. .. ..$ time_quarter : num [1:7655] NA NA NA NA NA NA NA NA NA NA ...
## .. .. .. .. .. ..$ time_month : num [1:7655] NA -0.05 NA -0.044 NA -0.012 NA NA -0.741 NA ...
## .. .. .. .. .. ..$ time_day : num [1:7655] NA 0.042 0.032 0.008 NA 0.005 NA NA -0.024 NA ...
## .. .. .. .. .. ..$ time_wday : num [1:7655] NA -0.05 -0.035 NA NA NA NA NA NA NA ...
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## .. .. .. ..$ lvl : NULL
## .. .. .. ..$ spec :List of 7
## .. .. .. .. ..$ args :List of 7
## .. .. .. .. .. ..$ seasonal_period: language ~NULL
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ non_seasonal_ar: language ~2
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ seasonal_ar : language ~1
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ hidden_units : language ~10
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ num_networks : language ~10
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ penalty : language ~10
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ epochs : language ~50
## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. ..$ eng_args : Named list()
## .. .. .. .. .. ..- attr(*, "class")= chr [1:2] "quosures" "list"
## .. .. .. .. ..$ mode : chr "regression"
## .. .. .. .. ..$ user_specified_mode : logi TRUE
## .. .. .. .. ..$ method :List of 3
## .. .. .. .. .. ..$ libs: chr [1:2] "forecast" "modeltime"
## .. .. .. .. .. ..$ fit :List of 5
## .. .. .. .. .. .. ..$ interface: chr "data.frame"
## .. .. .. .. .. .. ..$ protect : chr [1:2] "x" "y"
## .. .. .. .. .. .. ..$ func : Named chr "nnetar_fit_impl"
## .. .. .. .. .. .. .. ..- attr(*, "names")= chr "fun"
## .. .. .. .. .. .. ..$ defaults : list()
## .. .. .. .. .. .. ..$ args :List of 8
## .. .. .. .. .. .. .. ..$ x : language missing_arg()
## .. .. .. .. .. .. .. ..$ y : language missing_arg()
## .. .. .. .. .. .. .. ..$ p : language ~2
## .. .. .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. .. .. ..$ P : language ~1
## .. .. .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. .. .. ..$ size : language ~10
## .. .. .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. .. .. ..$ repeats: language ~10
## .. .. .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. .. .. ..$ decay : language ~10
## .. .. .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. .. .. ..$ maxit : language ~50
## .. .. .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## .. .. .. .. .. ..$ pred:List of 1
## .. .. .. .. .. .. ..$ numeric:List of 4
## .. .. .. .. .. .. .. ..$ pre : NULL
## .. .. .. .. .. .. .. ..$ post: NULL
## .. .. .. .. .. .. .. ..$ func: Named chr "predict"
## .. .. .. .. .. .. .. .. ..- attr(*, "names")= chr "fun"
## .. .. .. .. .. .. .. ..$ args:List of 2
## .. .. .. .. .. .. .. .. ..$ object : language object$fit
## .. .. .. .. .. .. .. .. ..$ new_data: symbol new_data
## .. .. .. .. ..$ engine : chr "nnetar"
## .. .. .. .. ..$ user_specified_engine: logi TRUE
## .. .. .. .. ..- attr(*, "class")= chr [1:2] "nnetar_reg" "model_spec"
## .. .. .. ..$ fit :List of 4
## .. .. .. .. ..$ models:List of 1
## .. .. .. .. .. ..$ model_1:List of 17
## .. .. .. .. .. .. ..$ x : Time-Series [1:2979] from 1 to 597: 6.88 6.88 6.41 6.16 6.16 ...
## .. .. .. .. .. .. ..$ m : num 5
## .. .. .. .. .. .. ..$ p : num 2
## .. .. .. .. .. .. ..$ P : num 1
## .. .. .. .. .. .. ..$ scalex :List of 2
## .. .. .. .. .. .. .. ..$ center: num 7.14
## .. .. .. .. .. .. .. ..$ scale : num 0.893
## .. .. .. .. .. .. ..$ scalexreg:List of 2
## .. .. .. .. .. .. .. ..$ center: Named num [1:16] 1510064599.4 2017.35 1.51 2.51 6.52 ...
## .. .. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:16] "time_index_num" "time_year" "time_half" "time_quarter" ...
## .. .. .. .. .. .. .. ..$ scale : Named num [1:16] 107972612.19 3.42 0.5 1.12 3.45 ...
## .. .. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:16] "time_index_num" "time_year" "time_half" "time_quarter" ...
## .. .. .. .. .. .. ..$ size : num 10
## .. .. .. .. .. .. ..$ xreg : num [1:2979, 1:16] 1322524800 1322611200 1322697600 1322784000 1323043200 ...
## .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ : chr [1:16] "time_index_num" "time_year" "time_half" "time_quarter" ...
## .. .. .. .. .. .. ..$ subset : int [1:2979] 1 2 3 4 5 6 7 8 9 10 ...
## .. .. .. .. .. .. ..$ model :List of 10
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 264
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.255 -0.591 -0.729 -0.563 0.479 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.03 -1.15 -1.29 -1.32 -1.07 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.2163 -0.0952 0.0789 0.2886 0.0725 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 139
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.1043 -0.4863 -0.2448 -0.086 -0.0122 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.02 -1.09 -1.17 -1.18 -1.11 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.2292 -0.1591 -0.0414 0.1459 0.1129 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 318
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.275 0.87 1.226 0.848 -0.256 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.09 -1.22 -1.29 -1.28 -1.13 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.1535 -0.0233 0.0782 0.241 0.1262 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 246
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.0547 0.6304 0.3603 0.1676 -0.1326 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -0.999 -1.072 -1.18 -1.197 -1.072 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.247 -0.176 -0.033 0.162 0.072 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 124
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.00225 -0.61109 -0.33115 -0.07935 -0.02567 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.07 -1.15 -1.23 -1.24 -1.11 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.1741 -0.0986 0.0218 0.2096 0.1099 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 436
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.6 0.49 0.535 0.578 -0.438 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.15 -1.23 -1.28 -1.28 -1.09 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.1005 -0.015 0.0635 0.2482 0.0924 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 130
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.036 0.0415 0.0346 0.0292 -0.1853 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.05 -1.13 -1.22 -1.23 -1.07 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.20123 -0.11577 0.00635 0.19423 0.06762 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 122
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.0526 0.055 0.0563 -0.121 0.2697 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.01 -1.09 -1.2 -1.22 -1.11 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.2316 -0.1567 -0.0165 0.1803 0.1098 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 123
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.2012 0.3794 0.3044 0.2384 -0.0288 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.05 -1.13 -1.22 -1.23 -1.09 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.1968 -0.1144 0.0108 0.198 0.0851 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..$ :List of 15
## .. .. .. .. .. .. .. .. ..$ n : num [1:3] 19 10 1
## .. .. .. .. .. .. .. .. ..$ nunits : int 31
## .. .. .. .. .. .. .. .. ..$ nconn : num [1:32] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. .. .. .. .. .. ..$ conn : num [1:211] 0 1 2 3 4 5 6 7 8 9 ...
## .. .. .. .. .. .. .. .. ..$ nsunits : num 30
## .. .. .. .. .. .. .. .. ..$ decay : num 10
## .. .. .. .. .. .. .. .. ..$ entropy : logi FALSE
## .. .. .. .. .. .. .. .. ..$ softmax : logi FALSE
## .. .. .. .. .. .. .. .. ..$ censored : logi FALSE
## .. .. .. .. .. .. .. .. ..$ value : num 130
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.1523 -0.3737 -0.2355 -0.122 -0.0201 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2974, 1] -1.07 -1.16 -1.24 -1.26 -1.14 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2974, 1] -0.1731 -0.0915 0.0297 0.2209 0.1406 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ call : language nnet.default(x = x, y = y, size = ..1, linout = linout, decay = ..2, maxit = ..3, trace = trace)
## .. .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnet"
## .. .. .. .. .. .. .. ..- attr(*, "class")= chr "nnetarmodels"
## .. .. .. .. .. .. ..$ nnetargs :List of 2
## .. .. .. .. .. .. .. ..$ decay: num 10
## .. .. .. .. .. .. .. ..$ maxit: num 50
## .. .. .. .. .. .. ..$ fitted : Time-Series [1:2979] from 1 to 597: NA NA NA NA NA ...
## .. .. .. .. .. .. ..$ residuals: Time-Series [1:2979] from 1 to 597: NA NA NA NA NA ...
## .. .. .. .. .. .. ..$ lags : num [1:3] 1 2 5
## .. .. .. .. .. .. ..$ series : chr "outcome"
## .. .. .. .. .. .. ..$ method : chr "NNAR(2,1,10)[5]"
## .. .. .. .. .. .. ..$ call : language forecast::nnetar(y = outcome, p = p, P = P, size = size, repeats = repeats, xreg = xreg_matrix, decay = deca| __truncated__
## .. .. .. .. .. .. ..- attr(*, "class")= chr "nnetar"
## .. .. .. .. ..$ data : tibble [2,979 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. .. ..$ time : Date[1:2979], format: "2011-11-29" ...
## .. .. .. .. .. ..$ .actual : num [1:2979] 6.88 6.88 6.41 6.16 6.16 ...
## .. .. .. .. .. ..$ .fitted : num [1:2979] NA NA NA NA NA ...
## .. .. .. .. .. ..$ .residuals: num [1:2979] NA NA NA NA NA ...
## .. .. .. .. ..$ extras:List of 1
## .. .. .. .. .. ..$ xreg_recipe:List of 9
## .. .. .. .. .. .. ..$ var_info : tibble [17 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. .. .. .. ..$ variable: chr [1:17] "time" "time_index.num" "time_year" "time_half" ...
## .. .. .. .. .. .. .. ..$ type :List of 17
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## .. .. .. ..$ .pred : num [1:64] 7.35 7.4 7.43 7.45 7.45 ...
## .. .. .. ..$ .row : int [1:64] 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 ...
## .. .. .. ..$ close : num [1:64] 7.1 7.12 7.14 7.14 7.18 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [64 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:64] 7.39 7.38 7.38 7.36 7.34 ...
## .. .. .. ..$ .row : int [1:64] 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 ...
## .. .. .. ..$ close : num [1:64] 7.52 7.51 7.55 7.55 7.59 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [64 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:64] 6.98 7 7.02 7.05 7.07 ...
## .. .. .. ..$ .row : int [1:64] 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 ...
## .. .. .. ..$ close : num [1:64] 6.93 6.96 7 6.98 7 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [64 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:64] 7.23 7.26 7.31 7.31 7.31 ...
## .. .. .. ..$ .row : int [1:64] 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 ...
## .. .. .. ..$ close : num [1:64] 6.96 6.87 6.97 6.86 6.88 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. ..- attr(*, "parameters")= paramtrs [0 × 6] (S3: parameters/tbl_df/tbl/data.frame)
## .. .. ..$ name : chr(0)
## .. .. ..$ id : chr(0)
## .. .. ..$ source : chr(0)
## .. .. ..$ component : chr(0)
## .. .. ..$ component_id: chr(0)
## .. .. ..$ object : list()
## .. ..- attr(*, "metrics")=function (data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
## .. .. ..- attr(*, "class")= chr [1:3] "numeric_metric_set" "metric_set" "function"
## .. .. ..- attr(*, "metrics")=List of 1
## .. .. .. ..$ rmse:function (data, ...)
## .. .. .. .. ..- attr(*, "direction")= chr "minimize"
## .. .. .. .. ..- attr(*, "class")= chr [1:3] "numeric_metric" "metric" "function"
## .. ..- attr(*, "outcomes")= chr "close"
## .. ..- attr(*, "rset_info")=List of 2
## .. .. ..$ att :List of 8
## .. .. .. ..$ class : chr "time_series_cv"
## .. .. .. ..$ initial : num 252
## .. .. .. ..$ assess : num 64
## .. .. .. ..$ cumulative : logi FALSE
## .. .. .. ..$ skip : num 252
## .. .. .. ..$ lag : num 0
## .. .. .. ..$ slice_limit: num 5
## .. .. .. ..$ fingerprint: chr "ed270d80c0e4efa2eb273ba7489ab90f"
## .. .. ..$ label: chr NA
## ..$ : rsmp[+] (S3: resample_results/tune_results/tbl_df/tbl/data.frame)
## .. ..$ splits :List of 5
## .. .. ..$ :List of 4
## .. .. .. ..$ data :'data.frame': 2235 obs. of 2 variables:
## .. .. .. .. ..$ time : Date[1:2235], format: "2011-11-29" ...
## .. .. .. .. ..$ close: num [1:2235] 6.88 6.88 6.41 6.16 6.16 ...
## .. .. .. ..$ in_id : int [1:252] 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 ...
## .. .. .. ..$ out_id: int [1:64] 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 ...
## .. .. .. ..$ id : tibble [1 × 1] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. ..$ id: chr "Slice1"
## .. .. .. ..- attr(*, "class")= chr [1:2] "ts_cv_split" "rsplit"
## .. .. ..$ :List of 4
## .. .. .. ..$ data :'data.frame': 2235 obs. of 2 variables:
## .. .. .. .. ..$ time : Date[1:2235], format: "2011-11-29" ...
## .. .. .. .. ..$ close: num [1:2235] 6.88 6.88 6.41 6.16 6.16 ...
## .. .. .. ..$ in_id : int [1:252] 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 ...
## .. .. .. ..$ out_id: int [1:64] 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 ...
## .. .. .. ..$ id : tibble [1 × 1] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. ..$ id: chr "Slice2"
## .. .. .. ..- attr(*, "class")= chr [1:2] "ts_cv_split" "rsplit"
## .. .. ..$ :List of 4
## .. .. .. ..$ data :'data.frame': 2235 obs. of 2 variables:
## .. .. .. .. ..$ time : Date[1:2235], format: "2011-11-29" ...
## .. .. .. .. ..$ close: num [1:2235] 6.88 6.88 6.41 6.16 6.16 ...
## .. .. .. ..$ in_id : int [1:252] 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 ...
## .. .. .. ..$ out_id: int [1:64] 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 ...
## .. .. .. ..$ id : tibble [1 × 1] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. ..$ id: chr "Slice3"
## .. .. .. ..- attr(*, "class")= chr [1:2] "ts_cv_split" "rsplit"
## .. .. ..$ :List of 4
## .. .. .. ..$ data :'data.frame': 2235 obs. of 2 variables:
## .. .. .. .. ..$ time : Date[1:2235], format: "2011-11-29" ...
## .. .. .. .. ..$ close: num [1:2235] 6.88 6.88 6.41 6.16 6.16 ...
## .. .. .. ..$ in_id : int [1:252] 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 ...
## .. .. .. ..$ out_id: int [1:64] 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 ...
## .. .. .. ..$ id : tibble [1 × 1] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. ..$ id: chr "Slice4"
## .. .. .. ..- attr(*, "class")= chr [1:2] "ts_cv_split" "rsplit"
## .. .. ..$ :List of 4
## .. .. .. ..$ data :'data.frame': 2235 obs. of 2 variables:
## .. .. .. .. ..$ time : Date[1:2235], format: "2011-11-29" ...
## .. .. .. .. ..$ close: num [1:2235] 6.88 6.88 6.41 6.16 6.16 ...
## .. .. .. ..$ in_id : int [1:252] 912 913 914 915 916 917 918 919 920 921 ...
## .. .. .. ..$ out_id: int [1:64] 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 ...
## .. .. .. ..$ id : tibble [1 × 1] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. ..$ id: chr "Slice5"
## .. .. .. ..- attr(*, "class")= chr [1:2] "ts_cv_split" "rsplit"
## .. ..$ id : chr [1:5] "Slice1" "Slice2" "Slice3" "Slice4" ...
## .. ..$ .metrics :List of 5
## .. .. ..$ : tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .metric : chr "rmse"
## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 0.588
## .. .. .. ..$ .config : chr "Preprocessor1_Model1"
## .. .. ..$ : tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .metric : chr "rmse"
## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 0.223
## .. .. .. ..$ .config : chr "Preprocessor1_Model1"
## .. .. ..$ : tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .metric : chr "rmse"
## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 0.23
## .. .. .. ..$ .config : chr "Preprocessor1_Model1"
## .. .. ..$ : tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .metric : chr "rmse"
## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 0.178
## .. .. .. ..$ .config : chr "Preprocessor1_Model1"
## .. .. ..$ : tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .metric : chr "rmse"
## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 0.527
## .. .. .. ..$ .config : chr "Preprocessor1_Model1"
## .. ..$ .notes :List of 5
## .. .. ..$ : tibble [0 × 3] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ location: chr(0)
## .. .. .. ..$ type : chr(0)
## .. .. .. ..$ note : chr(0)
## .. .. ..$ : tibble [0 × 3] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ location: chr(0)
## .. .. .. ..$ type : chr(0)
## .. .. .. ..$ note : chr(0)
## .. .. ..$ : tibble [0 × 3] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ location: chr(0)
## .. .. .. ..$ type : chr(0)
## .. .. .. ..$ note : chr(0)
## .. .. ..$ : tibble [0 × 3] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ location: chr(0)
## .. .. .. ..$ type : chr(0)
## .. .. .. ..$ note : chr(0)
## .. .. ..$ : tibble [0 × 3] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ location: chr(0)
## .. .. .. ..$ type : chr(0)
## .. .. .. ..$ note : chr(0)
## .. ..$ .predictions:List of 5
## .. .. ..$ : tibble [64 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:64] 6.99 6.98 6.97 6.93 6.92 ...
## .. .. .. ..$ .row : int [1:64] 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 ...
## .. .. .. ..$ close : num [1:64] 7.07 7.04 7.06 7.07 7.05 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [64 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:64] 7.4 7.4 7.39 7.39 7.37 ...
## .. .. .. ..$ .row : int [1:64] 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 ...
## .. .. .. ..$ close : num [1:64] 7.1 7.12 7.14 7.14 7.18 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [64 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:64] 7.67 7.68 7.69 7.72 7.73 ...
## .. .. .. ..$ .row : int [1:64] 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 ...
## .. .. .. ..$ close : num [1:64] 7.52 7.51 7.55 7.55 7.59 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [64 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:64] 6.91 6.91 6.9 6.9 6.91 ...
## .. .. .. ..$ .row : int [1:64] 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 ...
## .. .. .. ..$ close : num [1:64] 6.93 6.96 7 6.98 7 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [64 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:64] 7.05 7.04 7.03 7.02 7.01 ...
## .. .. .. ..$ .row : int [1:64] 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 ...
## .. .. .. ..$ close : num [1:64] 6.96 6.87 6.97 6.86 6.88 ...
## .. .. .. ..$ .config: chr [1:64] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. ..- attr(*, "parameters")= paramtrs [0 × 6] (S3: parameters/tbl_df/tbl/data.frame)
## .. .. ..$ name : chr(0)
## .. .. ..$ id : chr(0)
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## .. .. ..$ component_id: chr(0)
## .. .. ..$ object : list()
## .. ..- attr(*, "metrics")=function (data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
## .. .. ..- attr(*, "class")= chr [1:3] "numeric_metric_set" "metric_set" "function"
## .. .. ..- attr(*, "metrics")=List of 1
## .. .. .. ..$ rmse:function (data, ...)
## .. .. .. .. ..- attr(*, "direction")= chr "minimize"
## .. .. .. .. ..- attr(*, "class")= chr [1:3] "numeric_metric" "metric" "function"
## .. ..- attr(*, "outcomes")= chr "close"
## .. ..- attr(*, "rset_info")=List of 2
## .. .. ..$ att :List of 8
## .. .. .. ..$ class : chr "time_series_cv"
## .. .. .. ..$ initial : num 252
## .. .. .. ..$ assess : num 64
## .. .. .. ..$ cumulative : logi FALSE
## .. .. .. ..$ skip : num 252
## .. .. .. ..$ lag : num 0
## .. .. .. ..$ slice_limit: num 5
## .. .. .. ..$ fingerprint: chr "ed270d80c0e4efa2eb273ba7489ab90f"
## .. .. ..$ label: chr NA
Cubist Stacking
set.seed(123)
ensemble_fit_cubist_tscv <- submodels_resample %>%
ensemble_model_spec(
model_spec = cubist_rules(mode = "regression",
committees = tune(),
neighbors = tune(),
max_rules = tune()
) %>%
set_engine("Cubist"),
# kfold = 10,
grid = 10,
control = control_grid(
verbose = TRUE,
allow_par = TRUE
)
)
## ── Tuning Model Specification ───────────────────────────────────
## ℹ Performing 5-Fold Cross Validation.
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## ✔ Finished tuning Model Specification.
## ℹ Model Parameters:
## # A tibble: 1 × 9
## committees neighbors max_rules .metric .estimator mean n std_err .config
## <int> <int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 70 2 378 rmse standard 0.0688 5 0.00526 Prepro…
## ℹ Prediction Error Comparison:
## # A tibble: 11 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 0.952 PROPHET
## 2 10 0.389 PROPHET W/ XGBOOST ERRORS
## 3 2 0.356 GLMNET
## 4 3 0.303 KERNLAB
## 5 4 0.413 KERNLAB
## 6 5 0.623 KKNN
## 7 6 0.454 RANDOMFOREST
## 8 7 0.415 XGBOOST
## 9 8 0.458 CUBIST
## 10 9 0.512 NNAR(2,1,10)[5]
## 11 ensemble 0.0283 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
## ℹ Model Workflow:
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: cubist_rules()
##
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##
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##
## Call:
## cubist.default(x = x, y = y, committees = 70L, control
## = Cubist::cubistControl(rules = 378L))
##
## Number of samples: 320
## Number of predictors: 10
##
## Number of committees: 70
## Number of rules per committee: 10, 6, 7, 5, 7, 7, 6, 5, 4, 5, 8, 7, 4, 9, 4, 8, 3, 12, 8, 6 ...
##
##
## 29.67 sec elapsed
modeltime_table(
ensemble_fit_cubist_tscv
) %>%
modeltime_accuracy(testing(splits))
## # A tibble: 1 × 9
## .model_id .model_desc .type mae mape mase smape rmse rsq
## <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 ENSEMBLE (CUBIST STACK): … Test 0.139 2.08 4.38 2.10 0.169 0.861
XGBOOST Stack
set.seed(123)
ensemble_fit_xgboost_tscv <- submodels_resample %>%
ensemble_model_spec(boost_tree(mode = "regression",
# mtry = tune(),
trees = tune(),
min_n = tune(),
tree_depth = tune(),
learn_rate = tune(),
loss_reduction = tune()) %>%
set_engine("xgboost"),
grid = 10,
control = control_grid(
verbose = TRUE,
allow_par = TRUE
)
)
## ── Tuning Model Specification ───────────────────────────────────
## ℹ Performing 5-Fold Cross Validation.
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## ✔ Finished tuning Model Specification.
## ℹ Model Parameters:
## # A tibble: 1 × 11
## trees min_n tree_depth learn_rate loss_reduction .metric .estimator mean
## <int> <int> <int> <dbl> <dbl> <chr> <chr> <dbl>
## 1 1291 12 11 0.230 0.000130 rmse standard 0.0771
## # ℹ 3 more variables: n <int>, std_err <dbl>, .config <chr>
## ℹ Prediction Error Comparison:
## # A tibble: 11 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 0.952 PROPHET
## 2 10 0.389 PROPHET W/ XGBOOST ERRORS
## 3 2 0.356 GLMNET
## 4 3 0.303 KERNLAB
## 5 4 0.413 KERNLAB
## 6 5 0.623 KKNN
## 7 6 0.454 RANDOMFOREST
## 8 7 0.415 XGBOOST
## 9 8 0.458 CUBIST
## 10 9 0.512 NNAR(2,1,10)[5]
## 11 ensemble 0.00869 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
## ℹ Model Workflow:
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: boost_tree()
##
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##
## ── Model ───────────────────────────────────────────────────────────────────────
## ##### xgb.Booster
## raw: 1.8 Mb
## call:
## xgboost::xgb.train(params = list(eta = 0.229923571462474, max_depth = 11L,
## gamma = 0.000130024971167041, colsample_bytree = 1, colsample_bynode = 1,
## min_child_weight = 12L, subsample = 1), data = x$data, nrounds = 1291L,
## watchlist = x$watchlist, verbose = 0, nthread = 1, objective = "reg:squarederror")
## params (as set within xgb.train):
## eta = "0.229923571462474", max_depth = "11", gamma = "0.000130024971167041", colsample_bytree = "1", colsample_bynode = "1", min_child_weight = "12", subsample = "1", nthread = "1", objective = "reg:squarederror", validate_parameters = "TRUE"
## xgb.attributes:
## niter
## callbacks:
## cb.evaluation.log()
## # of features: 10
## niter: 1291
## nfeatures : 10
## evaluation_log:
## iter training_rmse
## 1 5.168808703
## 2 3.991416259
## ---
## 1290 0.008686332
## 1291 0.008686332
##
## 45.23 sec elapsed
modeltime_table(
ensemble_fit_xgboost_tscv
) %>%
modeltime_accuracy(testing(splits))
## # A tibble: 1 × 9
## .model_id .model_desc .type mae mape mase smape rmse rsq
## <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 ENSEMBLE (XGBOOST STACK):… Test 0.230 3.57 7.27 3.49 0.278 0.604
GLMNET Stack
set.seed(123)
ensemble_fit_glmnet_tscv <- submodels_resample %>%
ensemble_model_spec(
model_spec = linear_reg(
penalty = tune(),
mixture = tune()
) %>%
set_engine("glmnet"),
# kfolds = 10,
grid = 10,
control = control_grid(
verbose = TRUE,
allow_par = TRUE
)
)
## ── Tuning Model Specification ───────────────────────────────────
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## ✔ Finished tuning Model Specification.
## ℹ Model Parameters:
## # A tibble: 1 × 8
## penalty mixture .metric .estimator mean n std_err .config
## <dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 2.19e-10 0.835 rmse standard 0.151 5 0.00564 Preprocessor1_Model09
## ℹ Prediction Error Comparison:
## # A tibble: 11 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 0.952 PROPHET
## 2 10 0.389 PROPHET W/ XGBOOST ERRORS
## 3 2 0.356 GLMNET
## 4 3 0.303 KERNLAB
## 5 4 0.413 KERNLAB
## 6 5 0.623 KKNN
## 7 6 0.454 RANDOMFOREST
## 8 7 0.415 XGBOOST
## 9 8 0.458 CUBIST
## 10 9 0.512 NNAR(2,1,10)[5]
## 11 ensemble 0.149 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
## ℹ Model Workflow:
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: linear_reg()
##
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##
## ── Model ───────────────────────────────────────────────────────────────────────
##
## Call: glmnet::glmnet(x = maybe_matrix(x), y = y, family = "gaussian", alpha = ~0.83521875477978)
##
## Df %Dev Lambda
## 1 0 0.00 0.51330
## 2 1 10.58 0.46770
## 3 2 20.41 0.42620
## 4 3 29.61 0.38830
## 5 3 37.38 0.35380
## 6 3 43.90 0.32240
## 7 3 49.38 0.29370
## 8 3 53.97 0.26760
## 9 4 57.94 0.24390
## 10 4 61.66 0.22220
## 11 5 64.85 0.20250
## 12 5 67.93 0.18450
## 13 5 70.53 0.16810
## 14 5 72.73 0.15320
## 15 5 74.60 0.13950
## 16 5 76.17 0.12720
## 17 5 77.51 0.11590
## 18 5 78.64 0.10560
## 19 4 79.53 0.09619
## 20 4 80.26 0.08764
## 21 5 81.24 0.07985
## 22 5 82.26 0.07276
## 23 5 83.12 0.06630
## 24 6 83.87 0.06041
## 25 6 84.51 0.05504
## 26 6 85.06 0.05015
## 27 6 85.53 0.04570
## 28 6 85.92 0.04164
## 29 6 86.26 0.03794
## 30 6 86.55 0.03457
## 31 6 86.80 0.03150
## 32 6 87.00 0.02870
## 33 6 87.18 0.02615
## 34 6 87.33 0.02383
## 35 6 87.45 0.02171
## 36 6 87.55 0.01978
## 37 6 87.64 0.01802
## 38 7 87.76 0.01642
## 39 7 87.92 0.01496
## 40 8 88.09 0.01363
## 41 8 88.61 0.01242
## 42 8 89.06 0.01132
## 43 8 89.44 0.01031
## 44 8 89.76 0.00940
## 45 8 90.03 0.00856
## 46 8 90.26 0.00780
##
## ...
## and 35 more lines.
##
## 4.98 sec elapsed
modeltime_table(
ensemble_fit_glmnet_tscv
) %>%
modeltime_accuracy(testing(splits))
## # A tibble: 1 × 9
## .model_id .model_desc .type mae mape mase smape rmse rsq
## <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 ENSEMBLE (GLMNET STACK): … Test 0.267 4.10 8.43 4.22 0.314 0.899
RANDOM FOREST Stack
set.seed(123)
ensemble_fit_ranger_tscv <- submodels_resample %>%
ensemble_model_spec(
model_spec = rand_forest(
mode = "regression",
trees = tune(),
min_n = tune()
) %>%
set_engine("ranger"),
# kfolds = 10,
grid = 10,
control = control_grid(verbose = TRUE, allow_par = TRUE)
)
## ── Tuning Model Specification ───────────────────────────────────
## ℹ Performing 5-Fold Cross Validation.
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## ✔ Finished tuning Model Specification.
## ℹ Model Parameters:
## # A tibble: 1 × 8
## trees min_n .metric .estimator mean n std_err .config
## <int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 1986 2 rmse standard 0.0754 5 0.00630 Preprocessor1_Model09
## ℹ Prediction Error Comparison:
## # A tibble: 11 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 0.952 PROPHET
## 2 10 0.389 PROPHET W/ XGBOOST ERRORS
## 3 2 0.356 GLMNET
## 4 3 0.303 KERNLAB
## 5 4 0.413 KERNLAB
## 6 5 0.623 KKNN
## 7 6 0.454 RANDOMFOREST
## 8 7 0.415 XGBOOST
## 9 8 0.458 CUBIST
## 10 9 0.512 NNAR(2,1,10)[5]
## 11 ensemble 0.0282 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
## ℹ Model Workflow:
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: rand_forest()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 0 Recipe Steps
##
## ── Model ───────────────────────────────────────────────────────────────────────
## Ranger result
##
## Call:
## ranger::ranger(x = maybe_data_frame(x), y = y, num.trees = ~1986L, min.node.size = min_rows(~2L, x), num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1))
##
## Type: Regression
## Number of trees: 1986
## Sample size: 320
## Number of independent variables: 10
## Mtry: 3
## Target node size: 2
## Variable importance mode: none
## Splitrule: variance
## OOB prediction error (MSE): 0.005551274
## R squared (OOB): 0.9785192
##
## 15.23 sec elapsed
modeltime_table(
ensemble_fit_ranger_tscv
) %>%
modeltime_accuracy(testing(splits))
## # A tibble: 1 × 9
## .model_id .model_desc .type mae mape mase smape rmse rsq
## <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 ENSEMBLE (RANGER STACK): … Test 0.228 3.50 7.21 3.45 0.266 0.717