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 [761 × 4]>
## 2 2 <workflow> GLMNET Test <tibble [761 × 4]>
## 3 3 <workflow> KERNLAB Test <tibble [761 × 4]>
## 4 4 <workflow> KERNLAB Test <tibble [761 × 4]>
## 5 5 <workflow> KKNN Test <tibble [761 × 4]>
## 6 6 <workflow> RANDOMFOREST Test <tibble [761 × 4]>
## 7 7 <workflow> XGBOOST Test <tibble [761 × 4]>
## 8 8 <workflow> CUBIST Test <tibble [761 × 4]>
## 9 9 <workflow> NNAR(2,1,10)[5] Test <tibble [761 × 4]>
## 10 10 <workflow> PROPHET W/ XGBOOST ERRORS Test <tibble [761 × 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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## 40.94 sec elapsed
submodels_resample %>% write_rds("submodels_resample")
submodels_resample$.resample_results[[1]]$.predictions
## [[1]]
## # A tibble: 65 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 14236. 1471 14365 Preprocessor1_Model1
## 2 14255. 1472 14365 Preprocessor1_Model1
## 3 14241. 1473 14342 Preprocessor1_Model1
## 4 14253. 1474 14365 Preprocessor1_Model1
## 5 14255. 1475 14563 Preprocessor1_Model1
## 6 14240. 1476 14544 Preprocessor1_Model1
## 7 14248. 1477 14365 Preprocessor1_Model1
## 8 14223. 1478 14456 Preprocessor1_Model1
## 9 14224. 1479 14365 Preprocessor1_Model1
## 10 14216. 1480 14498 Preprocessor1_Model1
## # ℹ 55 more rows
##
## [[2]]
## # A tibble: 65 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 14146. 1220 14110 Preprocessor1_Model1
## 2 14151. 1221 14135 Preprocessor1_Model1
## 3 14138. 1222 14115 Preprocessor1_Model1
## 4 14145. 1223 14135 Preprocessor1_Model1
## 5 14134. 1224 14080 Preprocessor1_Model1
## 6 14142. 1225 14105 Preprocessor1_Model1
## 7 14153. 1226 14125 Preprocessor1_Model1
## 8 14147. 1227 14125 Preprocessor1_Model1
## 9 14160. 1228 14060 Preprocessor1_Model1
## 10 14156. 1229 13999 Preprocessor1_Model1
## # ℹ 55 more rows
##
## [[3]]
## # A tibble: 65 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 14225. 969 14173 Preprocessor1_Model1
## 2 14255. 970 14385 Preprocessor1_Model1
## 3 14286. 971 14325 Preprocessor1_Model1
## 4 14385. 972 14375 Preprocessor1_Model1
## 5 14418. 973 14375 Preprocessor1_Model1
## 6 14452. 974 14347 Preprocessor1_Model1
## 7 14474. 975 14380 Preprocessor1_Model1
## 8 14496. 976 14365 Preprocessor1_Model1
## 9 14557. 977 14320 Preprocessor1_Model1
## 10 14575. 978 14355 Preprocessor1_Model1
## # ℹ 55 more rows
##
## [[4]]
## # A tibble: 65 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 13387. 718 13348 Preprocessor1_Model1
## 2 13394. 719 13369 Preprocessor1_Model1
## 3 13405. 720 13315 Preprocessor1_Model1
## 4 13405. 721 13285 Preprocessor1_Model1
## 5 13405. 722 13323 Preprocessor1_Model1
## 6 13408. 723 13308 Preprocessor1_Model1
## 7 13413. 724 13315 Preprocessor1_Model1
## 8 13424. 725 13330 Preprocessor1_Model1
## 9 13423. 726 13310 Preprocessor1_Model1
## 10 13419. 727 13308 Preprocessor1_Model1
## # ℹ 55 more rows
##
## [[5]]
## # A tibble: 65 × 4
## .pred .row close .config
## <dbl> <int> <dbl> <chr>
## 1 12944. 467 13088 Preprocessor1_Model1
## 2 12932. 468 13105 Preprocessor1_Model1
## 3 12897. 469 13095 Preprocessor1_Model1
## 4 12864. 470 13107 Preprocessor1_Model1
## 5 12817. 471 13135 Preprocessor1_Model1
## 6 12808. 472 13165 Preprocessor1_Model1
## 7 12813. 473 13135 Preprocessor1_Model1
## 8 12797. 474 13100 Preprocessor1_Model1
## 9 12785. 475 13097 Preprocessor1_Model1
## 10 12814. 476 13010 Preprocessor1_Model1
## # ℹ 55 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
## .. .. .. .. ..$ growth : chr "linear"
## .. .. .. .. ..$ changepoints : POSIXct[1:25], format: "2014-12-23" ...
## .. .. .. .. ..$ n.changepoints : num 25
## .. .. .. .. ..$ changepoint.range : num 0.8
## .. .. .. .. ..$ yearly.seasonality : logi TRUE
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## .. .. .. .. .. .. ..$ numeric:List of 4
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## .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
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## .. .. .. .. .. .. ..$ : chr "optimal"
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## .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
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## .. .. .. .. .. ..$ : num [1:2296] 13345 13325 13343 13383 13313 ...
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## .. .. .. .. .. ..$ kernel: chr "optimal"
## .. .. .. .. .. ..$ k : int 50
## .. .. .. .. ..$ response : chr "continuous"
## .. .. .. .. ..$ distance : num 10
## .. .. .. .. ..$ call : language kknn::train.kknn(formula = ..y ~ ., data = data, ks = min_rows(50, data, 5), distance = ~10, kernel = ~"optimal")
## .. .. .. .. ..$ terms :Classes 'terms', 'formula' language ..y ~ time + time_index.num + time_year + time_half + time_quarter + time_month + time_day + time_wday + tim| __truncated__ ...
## .. .. .. .. .. .. ..- attr(*, "variables")= language list(..y, time, time_index.num, time_year, time_half, time_quarter, time_month, time_day, time_wday, time_md| __truncated__ ...
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## .. .. .. .. .. .. .. .. ..$ : chr [1:17] "time" "time_index.num" "time_year" "time_half" ...
## .. .. .. .. .. .. ..- attr(*, "term.labels")= chr [1:17] "time" "time_index.num" "time_year" "time_half" ...
## .. .. .. .. .. .. ..- attr(*, "order")= int [1:17] 1 1 1 1 1 1 1 1 1 1 ...
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## .. .. .. .. .. .. ..- attr(*, ".Environment")=<environment: 0x000001f273c5e090>
## .. .. .. .. .. .. ..- attr(*, "predvars")= language list(..y, time, time_index.num, time_year, time_half, time_quarter, time_month, time_day, time_wday, time_md| __truncated__ ...
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## .. .. .. .. .. ..$ time : num [1:2296] 16324 16325 16328 16329 16330 ...
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## .. .. .. .. .. ..$ ..y : num [1:2296] 11820 11818 11925 11960 11950 ...
## .. .. .. .. ..- attr(*, "class")= chr [1:2] "train.kknn" "kknn"
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## .. .. .. .. ..$ y_var: chr "close"
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## .. .. ..$ actions: Named list()
## .. .. ..- attr(*, "class")= chr [1:2] "stage_post" "stage"
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## .. .. .. .. .. .. .. ..$ role : chr "predictor"
## .. .. .. .. .. .. .. ..$ trained: logi FALSE
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## .. .. .. .. .. .. ..$ time : Date[1:1535], format: "2014-09-11" ...
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## .. .. .. .. ..$ time : num [1:2296] 16324 16325 16328 16329 16330 ...
## .. .. .. .. ..$ time_index.num: num [1:2296] 1410393600 1410480000 1410739200 1410825600 1410912000 ...
## .. .. .. .. ..$ time_year : int [1:2296] 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 ...
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## .. .. .. .. ..$ time_quarter : int [1:2296] 3 3 3 3 3 3 3 3 3 3 ...
## .. .. .. .. ..$ time_month : int [1:2296] 9 9 9 9 9 9 9 9 9 9 ...
## .. .. .. .. ..$ time_day : int [1:2296] 11 12 15 16 17 18 19 22 23 24 ...
## .. .. .. .. ..$ time_wday : int [1:2296] 5 6 2 3 4 5 6 2 3 4 ...
## .. .. .. .. ..$ time_mday : int [1:2296] 11 12 15 16 17 18 19 22 23 24 ...
## .. .. .. .. ..$ time_qday : int [1:2296] 73 74 77 78 79 80 81 84 85 86 ...
## .. .. .. .. ..$ time_yday : int [1:2296] 254 255 258 259 260 261 262 265 266 267 ...
## .. .. .. .. ..$ time_mweek : int [1:2296] 2 2 3 3 3 3 3 4 4 4 ...
## .. .. .. .. ..$ time_week : int [1:2296] 37 37 37 37 38 38 38 38 38 39 ...
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## .. .. .. .. ..$ time_mday7 : int [1:2296] 2 2 3 3 3 3 3 4 4 4 ...
## .. .. .. ..$ outcomes : tibble [2,296 × 1] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. ..$ close: num [1:2296] 11820 11818 11925 11960 11950 ...
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## .. .. .. .. ..$ time_month : int [1:2296] 9 9 9 9 9 9 9 9 9 9 ...
## .. .. .. .. ..$ time_day : int [1:2296] 11 12 15 16 17 18 19 22 23 24 ...
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## .. .. .. .. ..$ time_mday : int [1:2296] 11 12 15 16 17 18 19 22 23 24 ...
## .. .. .. .. ..$ time_qday : int [1:2296] 73 74 77 78 79 80 81 84 85 86 ...
## .. .. .. .. ..$ time_yday : int [1:2296] 254 255 258 259 260 261 262 265 266 267 ...
## .. .. .. .. ..$ time_mweek : int [1:2296] 2 2 3 3 3 3 3 4 4 4 ...
## .. .. .. .. ..$ time_week : int [1:2296] 37 37 37 37 38 38 38 38 38 39 ...
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## .. .. .. .. ..$ close: num [1:2296] 11820 11818 11925 11960 11950 ...
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## .. .. .. .. ..$ time_month : int [1:2296] 9 9 9 9 9 9 9 9 9 9 ...
## .. .. .. .. ..$ time_day : int [1:2296] 11 12 15 16 17 18 19 22 23 24 ...
## .. .. .. .. ..$ time_wday : int [1:2296] 5 6 2 3 4 5 6 2 3 4 ...
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## .. .. .. .. ..$ time_qday : int [1:2296] 73 74 77 78 79 80 81 84 85 86 ...
## .. .. .. .. ..$ time_yday : int [1:2296] 254 255 258 259 260 261 262 265 266 267 ...
## .. .. .. .. ..$ time_mweek : int [1:2296] 2 2 3 3 3 3 3 4 4 4 ...
## .. .. .. .. ..$ time_week : int [1:2296] 37 37 37 37 38 38 38 38 38 39 ...
## .. .. .. .. ..$ time_week2 : int [1:2296] 1 1 1 1 0 0 0 0 0 1 ...
## .. .. .. .. ..$ time_week3 : int [1:2296] 1 1 1 1 2 2 2 2 2 0 ...
## .. .. .. .. ..$ time_week4 : int [1:2296] 1 1 1 1 2 2 2 2 2 3 ...
## .. .. .. .. ..$ time_mday7 : int [1:2296] 2 2 3 3 3 3 3 4 4 4 ...
## .. .. .. ..$ outcomes : tibble [2,296 × 1] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. ..$ close: num [1:2296] 11820 11818 11925 11960 11950 ...
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## .. .. .. .. .. .. .. .. ..$ object : language object$fit
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## .. .. .. .. .. .. .. .. ..$ neighbors: language rules::get_neighbors(object$spec$args)
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## .. .. .. .. .. .. .. .. ..$ object : language object$fit
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## .. .. .. .. .. .. .. .. ..$ 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 "11820,16324,1410393600,2014,2,3,9,11,5,11,73,254,2,37,1,1,1,2\n11818,16325,1410480000,2014,2,3,9,12,6,12,74,255"| __truncated__
## .. .. .. .. ..$ names : chr "| Generated using R version 4.2.1 (2022-06-23 ucrt)\n| on Sat Nov 25 13:07:21 2023\noutcome.\n\noutcome: contin"| __truncated__
## .. .. .. .. ..$ caseWeights : logi FALSE
## .. .. .. .. ..$ model : chr "id=\"Cubist 2.07 GPL Edition 2023-11-25\"\nprec=\"0\" globalmean=\"14057.54\" extrap=\"1\" insts=\"0\" ceiling="| __truncated__
## .. .. .. .. ..$ output : chr "\nCubist [Release 2.07 GPL Edition] Sat Nov 25 13:07:21 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 3539
## .. .. .. .. ..$ committees : num 100
## .. .. .. .. ..$ maxd : num 3.8
## .. .. .. .. ..$ dims : int [1:2] 2296 17
## .. .. .. .. ..$ splits :'data.frame': 13810 obs. of 8 variables:
## .. .. .. .. .. ..$ committee : num [1:13810] 1 1 1 1 1 1 1 1 1 1 ...
## .. .. .. .. .. ..$ rule : num [1:13810] 1 2 2 2 2 3 3 4 4 5 ...
## .. .. .. .. .. ..$ variable : chr [1:13810] "time" "time" "time_yday" "time" ...
## .. .. .. .. .. ..$ dir : chr [1:13810] "<=" "<=" ">" ">" ...
## .. .. .. .. .. ..$ value : num [1:13810] 16359 16666 223 16359 347 ...
## .. .. .. .. .. ..$ category : chr [1:13810] "" "" "" "" ...
## .. .. .. .. .. ..$ type : chr [1:13810] "type2" "type2" "type2" "type2" ...
## .. .. .. .. .. ..$ percentile: num [1:13810] 0.0113 0.1024 0.5936 0.0113 0.9517 ...
## .. .. .. .. ..$ usage :'data.frame': 17 obs. of 3 variables:
## .. .. .. .. .. ..$ Conditions: num [1:17] 100 27 22 9 5 5 2 0 0 0 ...
## .. .. .. .. .. ..$ Model : num [1:17] 98 68 58 64 34 62 68 61 27 7 ...
## .. .. .. .. .. ..$ Variable : chr [1:17] "time" "time_yday" "time_qday" "time_month" ...
## .. .. .. .. ..$ call : language cubist.default(x = x, y = y, committees = 100, control = Cubist::cubistControl(rules = 1000))
## .. .. .. .. ..$ coefficients:'data.frame': 4978 obs. of 20 variables:
## .. .. .. .. .. ..$ (Intercept) : num [1:4978] -183219 -82173 271899 79263 -13745 ...
## .. .. .. .. .. ..$ time : num [1:4978] 11.95 5.62 -15.79 -4.06 1.58 ...
## .. .. .. .. .. ..$ time_index.num: num [1:4978] NA NA NA NA NA NA NA NA NA NA ...
## .. .. .. .. .. ..$ time_year : num [1:4978] NA NA NA NA NA ...
## .. .. .. .. .. ..$ time_half : num [1:4978] NA NA NA NA NA NA NA NA NA NA ...
## .. .. .. .. .. ..$ time_quarter : num [1:4978] NA NA NA NA NA 13 NA NA 426 NA ...
## .. .. .. .. .. ..$ time_month : num [1:4978] 17 2352 68 181 303 ...
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## .. .. .. .. .. ..$ user_specified_engine: logi TRUE
## .. .. .. .. .. ..- attr(*, "class")= chr [1:2] "nnetar_reg" "model_spec"
## .. .. .. .. ..$ formula: NULL
## .. .. .. .. ..- attr(*, "class")= chr [1:3] "action_model" "action_fit" "action"
## .. .. ..$ fit :List of 6
## .. .. .. ..$ 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 [1:2296] from 1 to 460: 11820 11818 11925 11960 11950 ...
## .. .. .. .. .. .. ..$ m : num 5
## .. .. .. .. .. .. ..$ p : num 2
## .. .. .. .. .. .. ..$ P : num 1
## .. .. .. .. .. .. ..$ scalex :List of 2
## .. .. .. .. .. .. .. ..$ center: num 14058
## .. .. .. .. .. .. .. ..$ scale : num 814
## .. .. .. .. .. .. ..$ scalexreg:List of 2
## .. .. .. .. .. .. .. ..$ center: Named num [1:16] 1553657782.58 2018.73 1.52 2.54 6.61 ...
## .. .. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:16] "time_index_num" "time_year" "time_half" "time_quarter" ...
## .. .. .. .. .. .. .. ..$ scale : Named num [1:16] 83213750.87 2.65 0.5 1.13 3.46 ...
## .. .. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:16] "time_index_num" "time_year" "time_half" "time_quarter" ...
## .. .. .. .. .. .. ..$ size : num 10
## .. .. .. .. .. .. ..$ xreg : num [1:2296, 1:16] 1410393600 1410480000 1410739200 1410825600 1410912000 ...
## .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ : chr [1:16] "time_index_num" "time_year" "time_half" "time_quarter" ...
## .. .. .. .. .. .. ..$ subset : int [1:2296] 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 102
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.0372 0.2984 0.2224 0.1187 0.0955 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.18 -2.17 -2.15 -2.15 -2.15 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.385 -0.405 -0.41 -0.415 -0.443 ...
## .. .. .. .. .. .. .. .. .. ..- 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 150
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.1195 -0.2502 -0.1771 -0.0993 0.0785 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.3 -2.29 -2.27 -2.27 -2.32 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.256 -0.286 -0.292 -0.299 -0.266 ...
## .. .. .. .. .. .. .. .. .. ..- 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 128
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.1312 -0.1972 -0.1359 -0.0443 -0.0448 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.12 -2.11 -2.09 -2.08 -2.1 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.437 -0.458 -0.474 -0.482 -0.492 ...
## .. .. .. .. .. .. .. .. .. ..- 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 145
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.0322 -0.2498 -0.2148 -0.1962 -0.2609 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.21 -2.2 -2.18 -2.17 -2.13 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.348 -0.37 -0.381 -0.391 -0.462 ...
## .. .. .. .. .. .. .. .. .. ..- 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 208
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.538 0.48 0.258 0.191 -0.194 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.42 -2.41 -2.4 -2.4 -2.34 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.141 -0.159 -0.163 -0.165 -0.25 ...
## .. .. .. .. .. .. .. .. .. ..- 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 173
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] -0.0618 -0.2371 -0.1937 -0.1548 -0.1304 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.19 -2.17 -2.15 -2.14 -2.17 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.373 -0.407 -0.415 -0.431 -0.422 ...
## .. .. .. .. .. .. .. .. .. ..- 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 241
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.0375 -0.8322 -0.7897 -0.7633 -0.7688 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.23 -2.21 -2.23 -2.23 -2.15 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.331 -0.36 -0.33 -0.34 -0.443 ...
## .. .. .. .. .. .. .. .. .. ..- 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 174
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.348 0.366 0.336 0.09 -0.303 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.2 -2.16 -2.25 -2.22 -2.2 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.364 -0.414 -0.316 -0.35 -0.388 ...
## .. .. .. .. .. .. .. .. .. ..- 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 92.9
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.00767 0.42733 0.26537 0.06841 -0.04323 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.22 -2.22 -2.19 -2.19 -2.2 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.336 -0.353 -0.371 -0.374 -0.393 ...
## .. .. .. .. .. .. .. .. .. ..- 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 94.4
## .. .. .. .. .. .. .. .. ..$ wts : num [1:211] 0.0589 0.2389 0.1976 0.1385 0.0172 ...
## .. .. .. .. .. .. .. .. ..$ convergence : int 1
## .. .. .. .. .. .. .. .. ..$ fitted.values: num [1:2291, 1] -2.26 -2.25 -2.22 -2.22 -2.23 ...
## .. .. .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. .. ..$ residuals : num [1:2291, 1] -0.304 -0.324 -0.342 -0.349 -0.361 ...
## .. .. .. .. .. .. .. .. .. ..- 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 [1:2296] from 1 to 460: NA NA NA NA NA ...
## .. .. .. .. .. .. ..$ residuals: Time-Series [1:2296] from 1 to 460: 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 [2,296 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. .. .. ..$ time : Date[1:2296], format: "2014-09-11" ...
## .. .. .. .. .. ..$ .actual : num [1:2296] 11820 11818 11925 11960 11950 ...
## .. .. .. .. .. ..$ .fitted : num [1:2296] NA NA NA NA NA ...
## .. .. .. .. .. ..$ .residuals: num [1:2296] NA NA NA NA NA ...
## .. .. .. .. ..$ extras:List of 1
## .. .. .. .. .. ..$ xreg_recipe:List of 9
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## .. .. .. .. .. .. ..$ close: num [1:1535] 11820 11818 11925 11960 11950 ...
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## .. .. .. ..$ .row : int [1:65] 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 ...
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## .. .. .. ..$ .row : int [1:65] 969 970 971 972 973 974 975 976 977 978 ...
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## .. .. ..$ : tibble [65 × 4] (S3: tbl_df/tbl/data.frame)
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## .. .. .. ..$ .row : int [1:65] 718 719 720 721 722 723 724 725 726 727 ...
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## .. .. ..$ : tibble [65 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:65] 13113 13119 13128 13157 13181 ...
## .. .. .. ..$ .row : int [1:65] 467 468 469 470 471 472 473 474 475 476 ...
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## .. .. .. .. ..$ close: num [1:1535] 11820 11818 11925 11960 11950 ...
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## .. .. .. ..$ data :'data.frame': 1535 obs. of 2 variables:
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## .. .. .. ..$ data :'data.frame': 1535 obs. of 2 variables:
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## .. .. .. .. ..$ close: num [1:1535] 11820 11818 11925 11960 11950 ...
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## .. ..$ id : chr [1:5] "Slice1" "Slice2" "Slice3" "Slice4" ...
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## .. .. .. ..$ .estimate : num 2427
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## .. .. .. ..$ .metric : chr "rmse"
## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 221
## .. .. .. ..$ .config : chr "Preprocessor1_Model1"
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## .. .. .. ..$ .metric : chr "rmse"
## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 443
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## .. .. ..$ : tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .metric : chr "rmse"
## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 83.9
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## .. .. .. ..$ .estimator: chr "standard"
## .. .. .. ..$ .estimate : num 131
## .. .. .. ..$ .config : chr "Preprocessor1_Model1"
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## .. .. .. ..$ .pred : num [1:65] 14087 14073 13616 13591 13535 ...
## .. .. .. ..$ .row : int [1:65] 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 ...
## .. .. .. ..$ close : num [1:65] 14365 14365 14342 14365 14563 ...
## .. .. .. ..$ .config: chr [1:65] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [65 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:65] 14218 14261 14262 14266 14260 ...
## .. .. .. ..$ .row : int [1:65] 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 ...
## .. .. .. ..$ close : num [1:65] 14110 14135 14115 14135 14080 ...
## .. .. .. ..$ .config: chr [1:65] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [65 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:65] 14148 14136 14138 14026 14025 ...
## .. .. .. ..$ .row : int [1:65] 969 970 971 972 973 974 975 976 977 978 ...
## .. .. .. ..$ close : num [1:65] 14173 14385 14325 14375 14375 ...
## .. .. .. ..$ .config: chr [1:65] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [65 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:65] 13387 13382 13384 13380 13357 ...
## .. .. .. ..$ .row : int [1:65] 718 719 720 721 722 723 724 725 726 727 ...
## .. .. .. ..$ close : num [1:65] 13348 13369 13315 13285 13323 ...
## .. .. .. ..$ .config: chr [1:65] "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" "Preprocessor1_Model1" ...
## .. .. ..$ : tibble [65 × 4] (S3: tbl_df/tbl/data.frame)
## .. .. .. ..$ .pred : num [1:65] 13095 13108 13117 13102 13088 ...
## .. .. .. ..$ .row : int [1:65] 467 468 469 470 471 472 473 474 475 476 ...
## .. .. .. ..$ close : num [1:65] 13088 13105 13095 13107 13135 ...
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## .. ..- attr(*, "rset_info")=List of 2
## .. .. ..$ att :List of 8
## .. .. .. ..$ class : chr "time_series_cv"
## .. .. .. ..$ initial : num 251
## .. .. .. ..$ assess : num 65
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## .. .. .. ..$ lag : num 0
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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 35 5 404 rmse standard 63.5 5 6.17 Preproc…
## ℹ Prediction Error Comparison:
## # A tibble: 11 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 343. PROPHET
## 2 10 1110. PROPHET W/ XGBOOST ERRORS
## 3 2 335. GLMNET
## 4 3 787. KERNLAB
## 5 4 320. KERNLAB
## 6 5 358. KKNN
## 7 6 405. RANDOMFOREST
## 8 7 369. XGBOOST
## 9 8 385. CUBIST
## 10 9 398. NNAR(2,1,10)[5]
## 11 ensemble 29.6 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
## ℹ Model Workflow:
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: cubist_rules()
##
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##
## ── Model ───────────────────────────────────────────────────────────────────────
##
## Call:
## cubist.default(x = x, y = y, committees = 35L, control
## = Cubist::cubistControl(rules = 404L))
##
## Number of samples: 325
## Number of predictors: 10
##
## Number of committees: 35
## Number of rules per committee: 8, 5, 6, 7, 6, 8, 4, 7, 7, 7, 5, 7, 6, 8, 12, 7, 11, 9, 11, 11 ...
##
##
## 32.16 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 723. 4.81 21.4 4.98 871. 0.00352
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 59.6
## # 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 1 343. PROPHET
## 2 10 1110. PROPHET W/ XGBOOST ERRORS
## 3 2 335. GLMNET
## 4 3 787. KERNLAB
## 5 4 320. KERNLAB
## 6 5 358. KKNN
## 7 6 405. RANDOMFOREST
## 8 7 369. XGBOOST
## 9 8 385. CUBIST
## 10 9 398. NNAR(2,1,10)[5]
## 11 ensemble 0.0115 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
## ℹ Model Workflow:
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: boost_tree()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
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##
## ── Model ───────────────────────────────────────────────────────────────────────
## ##### xgb.Booster
## raw: 2.1 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 10783.70555521
## 2 8320.57807311
## ---
## 1290 0.01154862
## 1291 0.01154862
##
## 45.41 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 584. 3.87 17.3 3.99 744. 0.137
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 ───────────────────────────────────
## ℹ Performing 5-Fold Cross Validation.
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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 0.852 0.0524 rmse standard 130. 5 3.94 Preprocessor1_Model01
## ℹ Prediction Error Comparison:
## # A tibble: 11 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 343. PROPHET
## 2 10 1110. PROPHET W/ XGBOOST ERRORS
## 3 2 335. GLMNET
## 4 3 787. KERNLAB
## 5 4 320. KERNLAB
## 6 5 358. KKNN
## 7 6 405. RANDOMFOREST
## 8 7 369. XGBOOST
## 9 8 385. CUBIST
## 10 9 398. NNAR(2,1,10)[5]
## 11 ensemble 124. ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
## ℹ Model Workflow:
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: linear_reg()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 0 Recipe Steps
##
## ── Model ───────────────────────────────────────────────────────────────────────
##
## Call: glmnet::glmnet(x = maybe_matrix(x), y = y, family = "gaussian", alpha = ~0.052367567793699)
##
## Df %Dev Lambda
## 1 0 0.00 11310.0
## 2 6 2.39 10300.0
## 3 8 6.88 9387.0
## 4 8 11.68 8553.0
## 5 8 16.44 7793.0
## 6 8 21.13 7101.0
## 7 8 25.72 6470.0
## 8 8 30.18 5895.0
## 9 8 34.49 5372.0
## 10 8 38.64 4894.0
## 11 8 42.60 4460.0
## 12 8 46.36 4063.0
## 13 8 49.90 3702.0
## 14 8 53.22 3374.0
## 15 8 56.32 3074.0
## 16 8 59.19 2801.0
## 17 8 61.83 2552.0
## 18 8 64.26 2325.0
## 19 8 66.48 2119.0
## 20 8 68.49 1930.0
## 21 8 70.32 1759.0
## 22 8 71.98 1603.0
## 23 8 73.47 1460.0
## 24 8 74.81 1331.0
## 25 8 76.02 1212.0
## 26 9 77.13 1105.0
## 27 9 78.42 1007.0
## 28 10 79.64 917.1
## 29 10 80.80 835.7
## 30 10 81.89 761.4
## 31 10 82.91 693.8
## 32 10 83.87 632.1
## 33 10 84.76 576.0
## 34 10 85.59 524.8
## 35 10 86.38 478.2
## 36 10 87.11 435.7
## 37 10 87.79 397.0
## 38 10 88.43 361.7
## 39 10 89.03 329.6
## 40 10 89.58 300.3
## 41 10 90.10 273.6
## 42 10 90.57 249.3
## 43 10 91.02 227.2
## 44 10 91.43 207.0
## 45 10 91.81 188.6
## 46 10 92.15 171.9
##
## ...
## and 54 more lines.
##
## 5.27 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 374. 2.57 11.1 2.53 420. 0.906
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 58.6 5 2.93 Preprocessor1_Model09
## ℹ Prediction Error Comparison:
## # A tibble: 11 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 343. PROPHET
## 2 10 1110. PROPHET W/ XGBOOST ERRORS
## 3 2 335. GLMNET
## 4 3 787. KERNLAB
## 5 4 320. KERNLAB
## 6 5 358. KKNN
## 7 6 405. RANDOMFOREST
## 8 7 369. XGBOOST
## 9 8 385. CUBIST
## 10 9 398. NNAR(2,1,10)[5]
## 11 ensemble 21.0 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
## ℹ Model Workflow:
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: rand_forest()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
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##
## ── 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: 325
## Number of independent variables: 10
## Mtry: 3
## Target node size: 2
## Variable importance mode: none
## Splitrule: variance
## OOB prediction error (MSE): 3104.022
## R squared (OOB): 0.9927881
##
## 15.72 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 541. 3.58 16.0 3.68 695. 0.0482