The Tidymodels Extension for GARCH models
Home Page: https://albertoalmuinha.github.io/garchmodels/
License: Other
Some facts about myself:
me <- list(
name = "Almuiña",
first_name = "Alberto",
occupation = "Data Scientist",
employer = "ABANCA",
favorite_programming_language = "R",
my_r_packages = c("boostime", "bayesmodels", "garchmodels", "neuralprophet",
"DSpoty", "shinyrecipes", "ROMDB", "rgenius")
)
📌 You can check my Blog where I talk about my experiences and the packages I develop 📖
During the tests that I did on my computer (following the tutorial provided in the repository), I had some problems, below I will put the code (and the result printed by the terminal after the execution of each block of code):
install.packages("garchmodels")
install.packages("timetk")
install.packages("tidymodels")
install.packages("tidyverse")
install.packages("devtools")
devtools::install_github("AlbertoAlmuinha/garchmodels")
library(garchmodels)
library(timetk)
library(tidymodels)
library(tidyverse)
rIBM
#> # A tibble: 3,523 x 2
#> date daily_returns
#>
#> 1 2007-01-03 0.000926
#> 2 2007-01-04 0.0107
#> 3 2007-01-05 -0.00905
#> 4 2007-01-08 0.0152
#> 5 2007-01-09 0.0118
#> 6 2007-01-10 -0.0118
#> 7 2007-01-11 -0.00243
#> 8 2007-01-12 0.00699
#> 9 2007-01-16 0.0149
#> 10 2007-01-17 -0.00793
#> # ... with 3,513 more rows
rIBM_extended <- rIBM %>%
future_frame(.length_out = 3, .bind_data = TRUE)
rIBM_train <- rIBM_extended %>% drop_na()
rIBM_future <- rIBM_extended %>% filter(is.na(daily_returns))
model_fit_garch <-garchmodels::garch_reg(mode = "regression",
arch_order = 1,
garch_order = 1,
ma_order = 0,
ar_order = 0) %>%
set_engine("rugarch", mean.model = list(include.mean = FALSE)) %>%
fit(daily_returns ~ date, data = rIBM_train)
plot(model_fit_garch$fit$models$model_1, which = 2)
#>
#> please wait...calculating quantiles...
predict(model_fit_garch, rIBM_future)
model_fit_garch <-garchmodels::garch_reg(mode = "regression",
arch_order = 1,
garch_order = 1,
ma_order = 2,
ar_order = 2) %>%
set_engine("rugarch") %>%
fit(daily_returns ~ date, data = rIBM_train)
plot(model_fit_garch$fit$models$model_1, which = 2)
plot(model_fit_garch$fit$models$model_1, which = 3)
predict(model_fit_garch, rIBM_future)
there is an instruction or library that was not in the tutorial ? : Getting Started with Garchmodels
(I would like to apologize for my issue being too long, I don't know any other way to communicate the other that I found. 👋)
Hello,
I would like to know if this package is still actively supported.
I wanted to give it a go since it is very interesting for me the possibility of automatically tuning a Garch model.
However, the package is no longer on CRAN and when I try to install it from github on my platform (Debian linux) here is what happens
> devtools::install_github("AlbertoAlmuinha/garchmodels")
Downloading GitHub repo AlbertoAlmuinha/garchmodels@HEAD
checking for file ‘/tmp/Rtmp6WO2o5/remotes27e32767473a3/AlbertoAlmuinha-garch✔ checking for file ‘/tmp/Rtmp6WO2o5/remotes27e32767473a3/AlbertoAlmuinha-garchmodels-2385a06/DESCRIPTION’
─ preparing ‘garchmodels’:
✔ checking DESCRIPTION meta-information ...
─ checking for LF line-endings in source and make files and shell scripts
─ checking for empty or unneeded directories
─ building ‘garchmodels_0.1.1.tar.gz’
Installing package into ‘/usr/local/lib/R/site-library’
(as ‘lib’ is unspecified)
* installing *source* package ‘garchmodels’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
*** copying figures
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
Error: package or namespace load failed for ‘garchmodels’:
.onLoad failed in loadNamespace() for 'garchmodels', details:
call: check_mode_for_new_engine(model, eng, mode)
error: 'regression' is not a known mode for model `garch_reg()`.
Error: loading failed
Execution halted
ERROR: loading failed
* removing ‘/usr/local/lib/R/site-library/garchmodels’
Warning message:
In i.p(...) :
installation of package ‘/tmp/Rtmp6WO2o5/file27e322c67e19e/garchmodels_0.1.1.tar.gz’ had non-zero exit status
> sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 11 (bullseye)
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] Rcpp_1.0.9 compiler_4.2.1 later_1.3.0 urlchecker_1.0.1
[5] prettyunits_1.1.1 profvis_0.3.7 remotes_2.4.2 tools_4.2.1
[9] digest_0.6.29 pkgbuild_1.3.1 pkgload_1.3.0 memoise_2.0.1
[13] lifecycle_1.0.1 rlang_1.0.4 shiny_1.7.1 cli_3.3.0
[17] curl_4.3.2 fastmap_1.1.0 withr_2.5.0 stringr_1.4.0
[21] fs_1.5.2 htmlwidgets_1.5.4 devtools_2.4.4 rprojroot_2.0.3
[25] glue_1.6.2 R6_2.5.1 processx_3.6.1 sessioninfo_1.2.2
[29] callr_3.7.0 purrr_0.3.4 magrittr_2.0.3 ps_1.7.1
[33] promises_1.2.0.1 ellipsis_0.3.2 htmltools_0.5.2 usethis_2.1.6
[37] mime_0.12 xtable_1.8-4 httpuv_1.6.5 stringi_1.7.8
[41] miniUI_0.1.1.1 cachem_1.0.6 crayon_1.5.1
>
Any idea about how I can fix this?
Thanks a lot.
Hello
Please have a look at the reprex below. It is essentially the example at
https://albertoalmuinha.github.io/garchmodels/articles/tuning_univariate_algorithms.html
I get some warning and error messages, in particular when tuning the model on various slices of the data.
Thanks a lot!
library(garchmodels)
#> Loading required package: parsnip
#> Loading required package: rugarch
#> Loading required package: parallel
#>
#> Attaching package: 'rugarch'
#> The following object is masked from 'package:stats':
#>
#> sigma
#> Loading required package: rmgarch
library(timetk)
library(tidyverse)
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
#> Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
#> Also defined by 'Rmpfr'
library(tidymodels)
rIBM
#> # A tibble: 3,523 × 2
#> date daily_returns
#> <date> <dbl>
#> 1 2007-01-03 0.000926
#> 2 2007-01-04 0.0107
#> 3 2007-01-05 -0.00905
#> 4 2007-01-08 0.0152
#> 5 2007-01-09 0.0118
#> 6 2007-01-10 -0.0118
#> 7 2007-01-11 -0.00243
#> 8 2007-01-12 0.00699
#> 9 2007-01-16 0.0149
#> 10 2007-01-17 -0.00793
#> # … with 3,513 more rows
#> # ℹ Use `print(n = ...)` to see more rows
rIBM_extended <- rIBM %>%
future_frame(.length_out = 3, .bind_data = TRUE)
#> .date_var is missing. Using: date
rIBM_train <- rIBM_extended %>% drop_na()
rIBM_future <- rIBM_extended %>% filter(is.na(daily_returns))
# Model Spec
model_spec <-garchmodels::garch_reg(mode = "regression",
arch_order = tune(),
garch_order = tune(),
tune_by = "sigmaFor") %>%
set_engine("rugarch")
# Recipe spec
recipe_spec <- recipe(daily_returns ~ date, data = rIBM_train)
# Workflow
wflw <- workflow() %>%
add_recipe(recipe_spec) %>%
add_model(model_spec)
resamples <- timetk::time_series_cv(rIBM_train,
date_var = date,
initial = "6 years",
assess = "24 months",
skip = "24 months",
cumulative = TRUE,
slice_limit = 3)
## timetk::plot_time_series_cv_plan(resamples, .date_var = date, .value = daily_returns)
tune_results <- tune_grid(
object = wflw,
resamples = resamples,
param_info = parameters(wflw),
grid = 5,
control = control_grid(verbose = TRUE, allow_par = TRUE, parallel_over = "everything")
)
#> Warning: `parameters.workflow()` was deprecated in tune 0.1.6.9003.
#> Please use `hardhat::extract_parameter_set_dials()` instead.
#> i Slice1: preprocessor 1/1
#> ✓ Slice1: preprocessor 1/1
#> i Slice1: preprocessor 1/1, model 1/5
#> frequency = 5 observations per 1 week
#> ✓ Slice1: preprocessor 1/1, model 1/5
#> i Slice1: preprocessor 1/1, model 1/5 (predictions)
#> i Slice1: preprocessor 1/1
#> ✓ Slice1: preprocessor 1/1
#> i Slice1: preprocessor 1/1, model 2/5
#> frequency = 5 observations per 1 week
#> ✓ Slice1: preprocessor 1/1, model 2/5
#> i Slice1: preprocessor 1/1, model 2/5 (predictions)
#> i Slice1: preprocessor 1/1
#> ✓ Slice1: preprocessor 1/1
#> i Slice1: preprocessor 1/1, model 3/5
#> frequency = 5 observations per 1 week
#> ✓ Slice1: preprocessor 1/1, model 3/5
#> i Slice1: preprocessor 1/1, model 3/5 (predictions)
#> i Slice1: preprocessor 1/1
#> ✓ Slice1: preprocessor 1/1
#> i Slice1: preprocessor 1/1, model 4/5
#> frequency = 5 observations per 1 week
#> x Slice1: preprocessor 1/1, model 4/5:
#> Error in `modeltime::new_modeltime_bridge()`:
#> ! 'data' does not have 4 columns
#>
#> 'data' should be a data frame (or tibble) containing 4 columns:
#> 1. date column (with name that matches input data)
#> 2. .actual (these are the original values your model trains from)
#> 3. .fitted (these are your model's in-sample training results)
#> 4. .residuals (these are your model's in-sample errors)
#> i Slice1: preprocessor 1/1
#> ✓ Slice1: preprocessor 1/1
#> i Slice1: preprocessor 1/1, model 5/5
#> frequency = 5 observations per 1 week
#> ✓ Slice1: preprocessor 1/1, model 5/5
#> i Slice1: preprocessor 1/1, model 5/5 (predictions)
#> i Slice2: preprocessor 1/1
#> ✓ Slice2: preprocessor 1/1
#> i Slice2: preprocessor 1/1, model 1/5
#> frequency = 5 observations per 1 week
#> ✓ Slice2: preprocessor 1/1, model 1/5
#> i Slice2: preprocessor 1/1, model 1/5 (predictions)
#> i Slice2: preprocessor 1/1
#> ✓ Slice2: preprocessor 1/1
#> i Slice2: preprocessor 1/1, model 2/5
#> frequency = 5 observations per 1 week
#> ✓ Slice2: preprocessor 1/1, model 2/5
#> i Slice2: preprocessor 1/1, model 2/5 (predictions)
#> i Slice2: preprocessor 1/1
#> ✓ Slice2: preprocessor 1/1
#> i Slice2: preprocessor 1/1, model 3/5
#> frequency = 5 observations per 1 week
#> ✓ Slice2: preprocessor 1/1, model 3/5
#> i Slice2: preprocessor 1/1, model 3/5 (predictions)
#> i Slice2: preprocessor 1/1
#> ✓ Slice2: preprocessor 1/1
#> i Slice2: preprocessor 1/1, model 4/5
#> frequency = 5 observations per 1 week
#> x Slice2: preprocessor 1/1, model 4/5:
#> Error in `modeltime::new_modeltime_bridge()`:
#> ! 'data' does not have 4 columns
#>
#> 'data' should be a data frame (or tibble) containing 4 columns:
#> 1. date column (with name that matches input data)
#> 2. .actual (these are the original values your model trains from)
#> 3. .fitted (these are your model's in-sample training results)
#> 4. .residuals (these are your model's in-sample errors)
#> i Slice2: preprocessor 1/1
#> ✓ Slice2: preprocessor 1/1
#> i Slice2: preprocessor 1/1, model 5/5
#> frequency = 5 observations per 1 week
#> ✓ Slice2: preprocessor 1/1, model 5/5
#> i Slice2: preprocessor 1/1, model 5/5 (predictions)
#> i Slice3: preprocessor 1/1
#> ✓ Slice3: preprocessor 1/1
#> i Slice3: preprocessor 1/1, model 1/5
#> frequency = 5 observations per 1 week
#> ✓ Slice3: preprocessor 1/1, model 1/5
#> i Slice3: preprocessor 1/1, model 1/5 (predictions)
#> i Slice3: preprocessor 1/1
#> ✓ Slice3: preprocessor 1/1
#> i Slice3: preprocessor 1/1, model 2/5
#> frequency = 5 observations per 1 week
#> ✓ Slice3: preprocessor 1/1, model 2/5
#> i Slice3: preprocessor 1/1, model 2/5 (predictions)
#> i Slice3: preprocessor 1/1
#> ✓ Slice3: preprocessor 1/1
#> i Slice3: preprocessor 1/1, model 3/5
#> frequency = 5 observations per 1 week
#> ✓ Slice3: preprocessor 1/1, model 3/5
#> i Slice3: preprocessor 1/1, model 3/5 (predictions)
#> i Slice3: preprocessor 1/1
#> ✓ Slice3: preprocessor 1/1
#> i Slice3: preprocessor 1/1, model 4/5
#> frequency = 5 observations per 1 week
#> ✓ Slice3: preprocessor 1/1, model 4/5
#> i Slice3: preprocessor 1/1, model 4/5 (predictions)
#> i Slice3: preprocessor 1/1
#> ✓ Slice3: preprocessor 1/1
#> i Slice3: preprocessor 1/1, model 5/5
#> frequency = 5 observations per 1 week
#> ✓ Slice3: preprocessor 1/1, model 5/5
#> i Slice3: preprocessor 1/1, model 5/5 (predictions)
tune_results %>% show_best(metric = "rmse")
#> # A tibble: 5 × 8
#> arch_order garch_order .metric .estimator mean n std_err .config
#> <int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
#> 1 1 0 rmse standard 0.0195 1 NA Preprocessor1…
#> 2 0 2 rmse standard 0.0199 3 0.00194 Preprocessor1…
#> 3 2 3 rmse standard 0.0212 3 0.00169 Preprocessor1…
#> 4 3 1 rmse standard 0.0212 3 0.00168 Preprocessor1…
#> 5 2 1 rmse standard 0.0212 3 0.00167 Preprocessor1…
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Debian GNU/Linux 11 (bullseye)
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
#> [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] parallel stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] yardstick_1.0.0 workflowsets_1.0.0 workflows_1.0.0 tune_1.0.0
#> [5] rsample_1.0.0 recipes_1.0.1 modeldata_1.0.0 infer_1.0.2
#> [9] dials_1.0.0 scales_1.2.0 broom_1.0.0 tidymodels_1.0.0
#> [13] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
#> [17] readr_2.1.2 tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6
#> [21] tidyverse_1.3.1 timetk_2.8.1 garchmodels_0.1.1 rmgarch_1.3-9
#> [25] rugarch_1.4-8 parsnip_1.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] colorspace_2.0-3 ellipsis_0.3.2
#> [3] class_7.3-20 mclust_5.4.10
#> [5] snakecase_0.11.0 corpcor_1.6.10
#> [7] fs_1.5.2 listenv_0.8.0
#> [9] furrr_0.3.0 spd_2.0-1
#> [11] prodlim_2019.11.13 fansi_1.0.3
#> [13] mvtnorm_1.1-3 lubridate_1.8.0
#> [15] xml2_1.3.3 codetools_0.2-18
#> [17] splines_4.2.1 R.methodsS3_1.8.2
#> [19] knitr_1.39 jsonlite_1.8.0
#> [21] Rmpfr_0.8-9 dbplyr_2.2.0
#> [23] R.oo_1.25.0 httr_1.4.3
#> [25] compiler_4.2.1 backports_1.4.1
#> [27] assertthat_0.2.1 Matrix_1.4-1
#> [29] fastmap_1.1.0 cli_3.3.0
#> [31] htmltools_0.5.2 tools_4.2.1
#> [33] gmp_0.6-6 gtable_0.3.0
#> [35] glue_1.6.2 Rcpp_1.0.9
#> [37] cellranger_1.1.0 styler_1.7.0
#> [39] DiceDesign_1.9 vctrs_0.4.1
#> [41] modeltime_1.2.2 iterators_1.0.14
#> [43] timeDate_3043.102 gower_1.0.0
#> [45] xfun_0.31 globals_0.16.0
#> [47] GeneralizedHyperbolic_0.8-4 rvest_1.0.2
#> [49] Bessel_0.6-0 lifecycle_1.0.1
#> [51] future_1.26.1 MASS_7.3-57
#> [53] zoo_1.8-10 ipred_0.9-13
#> [55] hms_1.1.1 DistributionUtils_0.6-0
#> [57] yaml_2.3.5 StanHeaders_2.21.0-7
#> [59] SkewHyperbolic_0.4-0 rpart_4.1.16
#> [61] stringi_1.7.8 highr_0.9
#> [63] pcaPP_2.0-2 foreach_1.5.2
#> [65] lhs_1.1.5 hardhat_1.2.0
#> [67] lava_1.6.10 truncnorm_1.0-8
#> [69] rlang_1.0.4 pkgconfig_2.0.3
#> [71] Rsolnp_1.16 pracma_2.3.8
#> [73] evaluate_0.15 lattice_0.20-45
#> [75] ks_1.13.5 bit_4.0.4
#> [77] tidyselect_1.1.2 parallelly_1.32.0
#> [79] magrittr_2.0.3 R6_2.5.1
#> [81] generics_0.1.3 DBI_1.1.3
#> [83] pillar_1.8.0 haven_2.5.0
#> [85] withr_2.5.0 xts_0.12.1
#> [87] survival_3.3-1 nnet_7.3-17
#> [89] future.apply_1.9.0 janitor_2.1.0
#> [91] modelr_0.1.8 crayon_1.5.1
#> [93] KernSmooth_2.23-20 utf8_1.2.2
#> [95] tzdb_0.3.0 rmarkdown_2.14
#> [97] readxl_1.4.0 grid_4.2.1
#> [99] reprex_2.0.1 digest_0.6.29
#> [101] R.cache_0.16.0 ff_4.0.7
#> [103] numDeriv_2016.8-1.1 R.utils_2.12.0
#> [105] RcppParallel_5.1.5 munsell_0.5.0
#> [107] GPfit_1.0-8Created on 2022-08-11 by the reprex package (v2.0.1)
-- nothing to show --Create dials parameters for arma order and garch order for univariate algorithm.
Hi everyone
I got the time series Problem.
I want to use multivar. GARCH model with binary predictor, where binary (dummy) variable being calendar feature like January (1 for Yes 0 for no)
Problem: I cannot fit calendar features as dummy variable.
Could someone suggest any useful packages with code example.
I tried to do it with garchmodells package.
Below is my code and error message:
DAX_rugarch_01_month <-
garch_multivariate_reg(
mode = "regression", type = "ugarchspec"
) %>%
set_engine("rugarch", specs= list(
spec1 = list(mean.model = list(armaOrder = c(1,0))),
spec2 = list(mean.model = list(armaOrder = c(1,0))),
spec3 = list(mean.model = list(armaOrder = c(1,0)))
)) %>%
fit(Return~ Date + January, data = DAX_train_January)
Error:
multifit GARCH-->error: speclist length not equal to data length
Somethiny is off with my specs at set_engines? The number of dimensions is 3, so I got spec1, spec2, spec3.
Would appreciate any help!
Already tried to reinstall R, Rstudio and all packages but still getting this error...
library(garchmodels)
Loading required package: parsnip
Loading required package: rugarch
Loading required package: parallel
Attaching package: ‘rugarch’
The following object is masked from ‘package:stats’:
sigma
Loading required package: rmgarch
Error: package or namespace load failed for ‘garchmodels’:
.onLoad failed in loadNamespace() for 'garchmodels', details:
call: check_model_doesnt_exist(model)
error: Model `garch_reg` already exists
Try to unificate multivariate engines because they work very similar but changing functions. Think about introduce something like a "type" parameter to select the type of multivariate algorithm.
Markov-Switching Models through MSGARGH 📦
Log Garch Models through the lgarch 📦
Add support for the rugarch engine to be able to tune and think if something similar could be done with some other engine (it would be complicated because it would imply changing the prediction output).
if(!require(pacman)) install.packages("pacman")
pacman::p_load(
"tidymodels",
"modeltime",
"dplyr",
"lubridate",
"timetk",
"odbc",
"DBI",
"janitor",
"tidyquant",
"modeltime.ensemble",
"modeltime.resample",
"modeltime.h2o"
)
library(bayesmodels)
library(garchmodels)Produces the following:
if(!require(pacman)) install.packages("pacman")
Loading required package: pacman
> pacman::p_load(
+ "tidymodels",
+ "modeltime",
+ "dplyr",
+ "lubridate",
+ "timetk",
+ "odbc",
+ "DBI",
+ "janitor",
+ "tidyquant",
+ "modeltime.ensemble",
+ "modeltime.resample",
+ "modeltime.h2o"
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
Registered S3 methods overwritten by 'bayesforecast':
method from
autoplot.ts forecast
forecast.ts forecast
fortify.ts forecast
print.garch tseries
+ )
> library(bayesmodels)
Loading required package: bayesforecast
Attaching package: ‘bayesforecast’
The following objects are masked from ‘package:base’:
beta, gamma
Loading required package: bsts
Loading required package: BoomSpikeSlab
Loading required package: Boom
Loading required package: MASS
Attaching package: ‘MASS’
The following object is masked from ‘package:dplyr’:
select
Attaching package: ‘Boom’
The following object is masked from ‘package:stats’:
rWishart
Attaching package: ‘BoomSpikeSlab’
The following object is masked from ‘package:stats’:
knots
Attaching package: ‘bsts’
The following object is masked from ‘package:BoomSpikeSlab’:
SuggestBurn
Attaching package: ‘bayesmodels’
The following objects are masked from ‘package:modeltime’:
non_seasonal_ar, non_seasonal_differences, non_seasonal_ma, seasonal_ar,
seasonal_differences, seasonal_ma
The following object is masked from ‘package:dials’:
tree_depth
Warning messages:
1: package ‘bayesmodels’ was built under R version 4.0.5
2: package ‘bayesforecast’ was built under R version 4.0.5
3: package ‘bsts’ was built under R version 4.0.5
4: package ‘BoomSpikeSlab’ was built under R version 4.0.5
5: package ‘Boom’ was built under R version 4.0.5
6: package ‘MASS’ was built under R version 4.0.5
> library(garchmodels)
Loading required package: rugarch
Loading required package: parallel
Attaching package: ‘rugarch’
The following object is masked from ‘package:bayesforecast’:
report
The following object is masked from ‘package:purrr’:
reduce
The following object is masked from ‘package:stats’:
sigma
Loading required package: rmgarch
Attaching package: ‘rmgarch’
The following objects are masked from ‘package:xts’:
first, last
The following objects are masked from ‘package:dplyr’:
first, last
Registered S3 methods overwritten by 'garchmodels':
method from
print.garch_reg bayesmodels
translate.garch_reg bayesmodels
update.garch_reg bayesmodels
Error: package or namespace load failed for ‘garchmodels’:
.onLoad failed in loadNamespace() for 'garchmodels', details:
call: NULL
error: Model `garch_reg` already exists
In addition: Warning messages:
1: package ‘garchmodels’ was built under R version 4.0.5
2: package ‘rugarch’ was built under R version 4.0.5
3: package ‘rmgarch’ was built under R version 4.0.5 
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