Re: [R] Issues with R's forecast function
Dear Sarah, I installed the latest R version available (4.4.0), installed the forecast package and related packages from scratch and the issue was resolved. Kind regards, Paul El lun, 27 may 2024 a las 13:51, Sarah Goslee () escribió: > Hi Paul, > > Looking at this, you aren't running the most recent version of forecast. > > If I were having a problem of this sort, I'd update R (if you can), > run update.packages() and then try again with a minimal set of > packages. As one of the other responses suggested, you probably have > mismatched versions of packages with dependencies. > > Sarah > > On Mon, May 27, 2024 at 2:48 PM Paul Bernal > wrote: > > > > Dear Sarah, > > > > Here is the sessionInfo() output, I forgot to include it in my reply. > > > > sessionInfo() > > R version 4.3.2 (2023-10-31 ucrt) > > Platform: x86_64-w64-mingw32/x64 (64-bit) > > Running under: Windows 11 x64 (build 22631) > > > > Matrix products: default > > > > > > locale: > > [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United > States.utf8 > > [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C > > [5] LC_TIME=English_United States.utf8 > > > > time zone: America/Bogota > > tzcode source: internal > > > > attached base packages: > > [1] parallel grid stats4stats graphics grDevices utils >datasets methods base > > > > other attached packages: > > [1] mvgam_1.1.1insight_0.19.7 > marginaleffects_0.20.1 brms_2.21.0 > > [5] mgcv_1.9-0 nlme_3.1-163 gbm_2.1.9 > yardstick_1.3.1 > > [9] workflowsets_1.1.0 workflows_1.1.4tune_1.2.1 > rsample_1.2.1 > > [13] recipes_1.0.10 parsnip_1.2.1 modeldata_1.3.0 > infer_1.0.7 > > [17] dials_1.2.1scales_1.3.0 broom_1.0.5 > tidymodels_1.2.0 > > [21] ggthemes_5.1.0 janitor_2.2.0 tictoc_1.2.1 > Ckmeans.1d.dp_4.3.5 > > [25] magrittr_2.0.3 data.table_1.14.10 reticulate_1.34.0 > tensorflow_2.15.0 > > [29] keras_2.13.0 matlabr_1.5.2 R.matlab_3.7.0 > distrMod_2.9.1 > > [33] RandVar_1.2.3 distrEx_2.9.2 distr_2.9.3 > sfsmisc_1.1-17 > > [37] startupmsg_0.9.6.1 qcc_2.7pdp_0.8.1 > doParallel_1.0.17 > > [41] iterators_1.0.14 foreach_1.5.2 tsintermittent_1.10 > ivreg_0.6-2 > > [45] vars_1.6-0 urca_1.3-3 strucchange_1.5-3 > Amelia_1.8.1 > > [49] Rcpp_1.0.12VIM_6.2.2 colorspace_2.1-0 > mi_1.1 > > [53] Hmisc_5.1-1missForest_1.5 mice_3.16.0 > gghighlight_0.4.1 > > [57] caret_6.0-94 lattice_0.21-9 xgboost_1.7.7.1 > smooth_4.0.0 > > [61] e1071_1.7-14 greybox_2.0.0 rio_1.0.1 > fitdistrplus_1.1-11 > > [65] AER_1.2-12 survival_3.5-7 sandwich_3.1-0 > lmtest_0.9-40 > > [69] zoo_1.8-12 car_3.1-2 carData_3.0-5 > forcats_1.0.0 > > [73] stringr_1.5.1 purrr_1.0.2readr_2.1.5 > tidyr_1.3.1 > > [77] tibble_3.2.1 tidyverse_2.0.0dplyr_1.1.4 > Metrics_0.1.4 > > [81] corrgram_1.14 corrplot_0.92 readxl_1.4.3 > glmnet_4.1-8 > > [85] Matrix_1.6-1.1 MASS_7.3-60.0.1actuar_3.3-4 > neuralnet_1.44.2 > > [89] nnfor_0.9.9generics_0.1.3 ggplot2_3.5.1 > lubridate_1.9.3 > > [93] tseries_0.10-55forecast_8.21.1 > > > > loaded via a namespace (and not attached): > > [1] matrixStats_1.3.0DiceDesign_1.10 httr_1.4.7 > RColorBrewer_1.1-3 tools_4.3.2 > > [6] doRNG_1.8.6 backports_1.4.1 utf8_1.2.4 > R6_2.5.1 jomo_2.7-6 > > [11] withr_3.0.0 sp_2.1-3 Brobdingnag_1.2-9 > gridExtra_2.3cli_3.6.2 > > [16] labeling_0.4.3 tsutils_0.9.4mvtnorm_1.2-4 > robustbase_0.99-2randomForest_4.7-1.1 > > [21] proxy_0.4-27 QuickJSR_1.1.3 StanHeaders_2.32.7 > foreign_0.8-85 R.utils_2.12.3 > > [26] parallelly_1.36.0scoringRules_1.1.1 itertools_0.1-3 > TTR_0.24.4 rstudioapi_0.16.0 > > [31] shape_1.4.6 distributional_0.4.0 inline_0.3.19 > loo_2.7.0fansi_1.0.6 > > [36] abind_1.4-5 R.methodsS3_1.8.2lifecycle_1.0.4 > multcomp_1.4-25 whisker_0.4.1 > > [41] snakecase_0.11.1 crayon_1.5.2 mitml_0.4-5 > zeallot_0.1.0pillar_1.9.0 > > [46] knitr_1.45 boot_1.3-28.1estimability_1.4.1 > future.apply_1.11.1 codetools_0.2-19 > > [51] pan_1.9 glue_1.7.0 vcd_1.4-12 > vctrs_0.6.5 png_0.1-8 > > [56] Rdpack_2.6 cellranger_1.1.0 gtable_0.3.4 > gower_1.0.1 xfun_0.41 > > [61] rbibutils_2.2.16 prodlim_2023.08.28 MAPA_2.0.6 > pracma_2.4.4 uroot_2.1-3 > > [66] coda_0.19-4.1timeDate_4032.109hardhat_1.3.1 > lava_1.7.3 statmod_1.5.0 > > [71] TH.data_1.1-2ipred_0.9-14 xts_0.13.1 > r
Re: [R] Issues with R's forecast function
Hi Sarah, I ran update.packages, reloaded the forecast package, but kept on getting the same errors. I am going to install the latest R version (4.4.0), as I was using version 4.3.2 and reinstall the package. Cheers, Paul El lun, 27 may 2024 a las 13:51, Sarah Goslee () escribió: > Hi Paul, > > Looking at this, you aren't running the most recent version of forecast. > > If I were having a problem of this sort, I'd update R (if you can), > run update.packages() and then try again with a minimal set of > packages. As one of the other responses suggested, you probably have > mismatched versions of packages with dependencies. > > Sarah > > On Mon, May 27, 2024 at 2:48 PM Paul Bernal > wrote: > > > > Dear Sarah, > > > > Here is the sessionInfo() output, I forgot to include it in my reply. > > > > sessionInfo() > > R version 4.3.2 (2023-10-31 ucrt) > > Platform: x86_64-w64-mingw32/x64 (64-bit) > > Running under: Windows 11 x64 (build 22631) > > > > Matrix products: default > > > > > > locale: > > [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United > States.utf8 > > [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C > > [5] LC_TIME=English_United States.utf8 > > > > time zone: America/Bogota > > tzcode source: internal > > > > attached base packages: > > [1] parallel grid stats4stats graphics grDevices utils >datasets methods base > > > > other attached packages: > > [1] mvgam_1.1.1insight_0.19.7 > marginaleffects_0.20.1 brms_2.21.0 > > [5] mgcv_1.9-0 nlme_3.1-163 gbm_2.1.9 > yardstick_1.3.1 > > [9] workflowsets_1.1.0 workflows_1.1.4tune_1.2.1 > rsample_1.2.1 > > [13] recipes_1.0.10 parsnip_1.2.1 modeldata_1.3.0 > infer_1.0.7 > > [17] dials_1.2.1scales_1.3.0 broom_1.0.5 > tidymodels_1.2.0 > > [21] ggthemes_5.1.0 janitor_2.2.0 tictoc_1.2.1 > Ckmeans.1d.dp_4.3.5 > > [25] magrittr_2.0.3 data.table_1.14.10 reticulate_1.34.0 > tensorflow_2.15.0 > > [29] keras_2.13.0 matlabr_1.5.2 R.matlab_3.7.0 > distrMod_2.9.1 > > [33] RandVar_1.2.3 distrEx_2.9.2 distr_2.9.3 > sfsmisc_1.1-17 > > [37] startupmsg_0.9.6.1 qcc_2.7pdp_0.8.1 > doParallel_1.0.17 > > [41] iterators_1.0.14 foreach_1.5.2 tsintermittent_1.10 > ivreg_0.6-2 > > [45] vars_1.6-0 urca_1.3-3 strucchange_1.5-3 > Amelia_1.8.1 > > [49] Rcpp_1.0.12VIM_6.2.2 colorspace_2.1-0 > mi_1.1 > > [53] Hmisc_5.1-1missForest_1.5 mice_3.16.0 > gghighlight_0.4.1 > > [57] caret_6.0-94 lattice_0.21-9 xgboost_1.7.7.1 > smooth_4.0.0 > > [61] e1071_1.7-14 greybox_2.0.0 rio_1.0.1 > fitdistrplus_1.1-11 > > [65] AER_1.2-12 survival_3.5-7 sandwich_3.1-0 > lmtest_0.9-40 > > [69] zoo_1.8-12 car_3.1-2 carData_3.0-5 > forcats_1.0.0 > > [73] stringr_1.5.1 purrr_1.0.2readr_2.1.5 > tidyr_1.3.1 > > [77] tibble_3.2.1 tidyverse_2.0.0dplyr_1.1.4 > Metrics_0.1.4 > > [81] corrgram_1.14 corrplot_0.92 readxl_1.4.3 > glmnet_4.1-8 > > [85] Matrix_1.6-1.1 MASS_7.3-60.0.1actuar_3.3-4 > neuralnet_1.44.2 > > [89] nnfor_0.9.9generics_0.1.3 ggplot2_3.5.1 > lubridate_1.9.3 > > [93] tseries_0.10-55forecast_8.21.1 > > > > loaded via a namespace (and not attached): > > [1] matrixStats_1.3.0DiceDesign_1.10 httr_1.4.7 > RColorBrewer_1.1-3 tools_4.3.2 > > [6] doRNG_1.8.6 backports_1.4.1 utf8_1.2.4 > R6_2.5.1 jomo_2.7-6 > > [11] withr_3.0.0 sp_2.1-3 Brobdingnag_1.2-9 > gridExtra_2.3cli_3.6.2 > > [16] labeling_0.4.3 tsutils_0.9.4mvtnorm_1.2-4 > robustbase_0.99-2randomForest_4.7-1.1 > > [21] proxy_0.4-27 QuickJSR_1.1.3 StanHeaders_2.32.7 > foreign_0.8-85 R.utils_2.12.3 > > [26] parallelly_1.36.0scoringRules_1.1.1 itertools_0.1-3 > TTR_0.24.4 rstudioapi_0.16.0 > > [31] shape_1.4.6 distributional_0.4.0 inline_0.3.19 > loo_2.7.0fansi_1.0.6 > > [36] abind_1.4-5 R.methodsS3_1.8.2lifecycle_1.0.4 > multcomp_1.4-25 whisker_0.4.1 > > [41] snakecase_0.11.1 crayon_1.5.2 mitml_0.4-5 > zeallot_0.1.0pillar_1.9.0 > > [46] knitr_1.45 boot_1.3-28.1estimability_1.4.1 > future.apply_1.11.1 codetools_0.2-19 > > [51] pan_1.9 glue_1.7.0 vcd_1.4-12 > vctrs_0.6.5 png_0.1-8 > > [56] Rdpack_2.6 cellranger_1.1.0 gtable_0.3.4 > gower_1.0.1 xfun_0.41 > > [61] rbibutils_2.2.16 prodlim_2023.08.28 MAPA_2.0.6 > pracma_2.4.4 uroot_2.1-3 > > [66] coda_0.19-4.1timeDate_4032.109hardhat_1.3.1 > lava_1.7.3 statmod_1.5.0 > > [71] TH.data_1.
Re: [R] Issues with R's forecast function
Hi Paul, Looking at this, you aren't running the most recent version of forecast. If I were having a problem of this sort, I'd update R (if you can), run update.packages() and then try again with a minimal set of packages. As one of the other responses suggested, you probably have mismatched versions of packages with dependencies. Sarah On Mon, May 27, 2024 at 2:48 PM Paul Bernal wrote: > > Dear Sarah, > > Here is the sessionInfo() output, I forgot to include it in my reply. > > sessionInfo() > R version 4.3.2 (2023-10-31 ucrt) > Platform: x86_64-w64-mingw32/x64 (64-bit) > Running under: Windows 11 x64 (build 22631) > > Matrix products: default > > > locale: > [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8 > [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C > [5] LC_TIME=English_United States.utf8 > > time zone: America/Bogota > tzcode source: internal > > attached base packages: > [1] parallel grid stats4stats graphics grDevices utils > datasets methods base > > other attached packages: > [1] mvgam_1.1.1insight_0.19.7 marginaleffects_0.20.1 > brms_2.21.0 > [5] mgcv_1.9-0 nlme_3.1-163 gbm_2.1.9 > yardstick_1.3.1 > [9] workflowsets_1.1.0 workflows_1.1.4tune_1.2.1 > rsample_1.2.1 > [13] recipes_1.0.10 parsnip_1.2.1 modeldata_1.3.0 > infer_1.0.7 > [17] dials_1.2.1scales_1.3.0 broom_1.0.5 > tidymodels_1.2.0 > [21] ggthemes_5.1.0 janitor_2.2.0 tictoc_1.2.1 > Ckmeans.1d.dp_4.3.5 > [25] magrittr_2.0.3 data.table_1.14.10 reticulate_1.34.0 > tensorflow_2.15.0 > [29] keras_2.13.0 matlabr_1.5.2 R.matlab_3.7.0 > distrMod_2.9.1 > [33] RandVar_1.2.3 distrEx_2.9.2 distr_2.9.3 > sfsmisc_1.1-17 > [37] startupmsg_0.9.6.1 qcc_2.7pdp_0.8.1 > doParallel_1.0.17 > [41] iterators_1.0.14 foreach_1.5.2 tsintermittent_1.10 > ivreg_0.6-2 > [45] vars_1.6-0 urca_1.3-3 strucchange_1.5-3 > Amelia_1.8.1 > [49] Rcpp_1.0.12VIM_6.2.2 colorspace_2.1-0 > mi_1.1 > [53] Hmisc_5.1-1missForest_1.5 mice_3.16.0 > gghighlight_0.4.1 > [57] caret_6.0-94 lattice_0.21-9 xgboost_1.7.7.1 > smooth_4.0.0 > [61] e1071_1.7-14 greybox_2.0.0 rio_1.0.1 > fitdistrplus_1.1-11 > [65] AER_1.2-12 survival_3.5-7 sandwich_3.1-0 > lmtest_0.9-40 > [69] zoo_1.8-12 car_3.1-2 carData_3.0-5 > forcats_1.0.0 > [73] stringr_1.5.1 purrr_1.0.2readr_2.1.5 > tidyr_1.3.1 > [77] tibble_3.2.1 tidyverse_2.0.0dplyr_1.1.4 > Metrics_0.1.4 > [81] corrgram_1.14 corrplot_0.92 readxl_1.4.3 > glmnet_4.1-8 > [85] Matrix_1.6-1.1 MASS_7.3-60.0.1actuar_3.3-4 > neuralnet_1.44.2 > [89] nnfor_0.9.9generics_0.1.3 ggplot2_3.5.1 > lubridate_1.9.3 > [93] tseries_0.10-55forecast_8.21.1 > > loaded via a namespace (and not attached): > [1] matrixStats_1.3.0DiceDesign_1.10 httr_1.4.7 > RColorBrewer_1.1-3 tools_4.3.2 > [6] doRNG_1.8.6 backports_1.4.1 utf8_1.2.4 R6_2.5.1 > jomo_2.7-6 > [11] withr_3.0.0 sp_2.1-3 Brobdingnag_1.2-9 > gridExtra_2.3cli_3.6.2 > [16] labeling_0.4.3 tsutils_0.9.4mvtnorm_1.2-4 > robustbase_0.99-2randomForest_4.7-1.1 > [21] proxy_0.4-27 QuickJSR_1.1.3 StanHeaders_2.32.7 > foreign_0.8-85 R.utils_2.12.3 > [26] parallelly_1.36.0scoringRules_1.1.1 itertools_0.1-3 > TTR_0.24.4 rstudioapi_0.16.0 > [31] shape_1.4.6 distributional_0.4.0 inline_0.3.19 > loo_2.7.0fansi_1.0.6 > [36] abind_1.4-5 R.methodsS3_1.8.2lifecycle_1.0.4 > multcomp_1.4-25 whisker_0.4.1 > [41] snakecase_0.11.1 crayon_1.5.2 mitml_0.4-5 > zeallot_0.1.0pillar_1.9.0 > [46] knitr_1.45 boot_1.3-28.1estimability_1.4.1 > future.apply_1.11.1 codetools_0.2-19 > [51] pan_1.9 glue_1.7.0 vcd_1.4-12 > vctrs_0.6.5 png_0.1-8 > [56] Rdpack_2.6 cellranger_1.1.0 gtable_0.3.4 > gower_1.0.1 xfun_0.41 > [61] rbibutils_2.2.16 prodlim_2023.08.28 MAPA_2.0.6 > pracma_2.4.4 uroot_2.1-3 > [66] coda_0.19-4.1timeDate_4032.109hardhat_1.3.1 > lava_1.7.3 statmod_1.5.0 > [71] TH.data_1.1-2ipred_0.9-14 xts_0.13.1 > rstan_2.32.6 tensorA_0.36.2.1 > [76] rpart_4.1.21 nnet_7.3-19
Re: [R] Issues with R's forecast function
Dear Sarah, Here is the sessionInfo() output, I forgot to include it in my reply. sessionInfo() R version 4.3.2 (2023-10-31 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 11 x64 (build 22631) Matrix products: default locale: [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8 [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C [5] LC_TIME=English_United States.utf8 time zone: America/Bogota tzcode source: internal attached base packages: [1] parallel grid stats4stats graphics grDevices utils datasets methods base other attached packages: [1] mvgam_1.1.1insight_0.19.7 marginaleffects_0.20.1 brms_2.21.0 [5] mgcv_1.9-0 nlme_3.1-163 gbm_2.1.9 yardstick_1.3.1 [9] workflowsets_1.1.0 workflows_1.1.4tune_1.2.1 rsample_1.2.1 [13] recipes_1.0.10 parsnip_1.2.1 modeldata_1.3.0 infer_1.0.7 [17] dials_1.2.1scales_1.3.0 broom_1.0.5 tidymodels_1.2.0 [21] ggthemes_5.1.0 janitor_2.2.0 tictoc_1.2.1 Ckmeans.1d.dp_4.3.5 [25] magrittr_2.0.3 data.table_1.14.10 reticulate_1.34.0 tensorflow_2.15.0 [29] keras_2.13.0 matlabr_1.5.2 R.matlab_3.7.0 distrMod_2.9.1 [33] RandVar_1.2.3 distrEx_2.9.2 distr_2.9.3 sfsmisc_1.1-17 [37] startupmsg_0.9.6.1 qcc_2.7pdp_0.8.1 doParallel_1.0.17 [41] iterators_1.0.14 foreach_1.5.2 tsintermittent_1.10 ivreg_0.6-2 [45] vars_1.6-0 urca_1.3-3 strucchange_1.5-3 Amelia_1.8.1 [49] Rcpp_1.0.12VIM_6.2.2 colorspace_2.1-0 mi_1.1 [53] Hmisc_5.1-1missForest_1.5 mice_3.16.0 gghighlight_0.4.1 [57] caret_6.0-94 lattice_0.21-9 xgboost_1.7.7.1 smooth_4.0.0 [61] e1071_1.7-14 greybox_2.0.0 rio_1.0.1 fitdistrplus_1.1-11 [65] AER_1.2-12 survival_3.5-7 sandwich_3.1-0 lmtest_0.9-40 [69] zoo_1.8-12 car_3.1-2 carData_3.0-5 forcats_1.0.0 [73] stringr_1.5.1 purrr_1.0.2readr_2.1.5 tidyr_1.3.1 [77] tibble_3.2.1 tidyverse_2.0.0dplyr_1.1.4 Metrics_0.1.4 [81] corrgram_1.14 corrplot_0.92 readxl_1.4.3 glmnet_4.1-8 [85] Matrix_1.6-1.1 MASS_7.3-60.0.1actuar_3.3-4 neuralnet_1.44.2 [89] nnfor_0.9.9generics_0.1.3 ggplot2_3.5.1 lubridate_1.9.3 [93] tseries_0.10-55forecast_8.21.1 loaded via a namespace (and not attached): [1] matrixStats_1.3.0DiceDesign_1.10 httr_1.4.7 RColorBrewer_1.1-3 tools_4.3.2 [6] doRNG_1.8.6 backports_1.4.1 utf8_1.2.4 R6_2.5.1 jomo_2.7-6 [11] withr_3.0.0 sp_2.1-3 Brobdingnag_1.2-9 gridExtra_2.3cli_3.6.2 [16] labeling_0.4.3 tsutils_0.9.4mvtnorm_1.2-4 robustbase_0.99-2randomForest_4.7-1.1 [21] proxy_0.4-27 QuickJSR_1.1.3 StanHeaders_2.32.7 foreign_0.8-85 R.utils_2.12.3 [26] parallelly_1.36.0scoringRules_1.1.1 itertools_0.1-3 TTR_0.24.4 rstudioapi_0.16.0 [31] shape_1.4.6 distributional_0.4.0 inline_0.3.19 loo_2.7.0fansi_1.0.6 [36] abind_1.4-5 R.methodsS3_1.8.2lifecycle_1.0.4 multcomp_1.4-25 whisker_0.4.1 [41] snakecase_0.11.1 crayon_1.5.2 mitml_0.4-5 zeallot_0.1.0pillar_1.9.0 [46] knitr_1.45 boot_1.3-28.1estimability_1.4.1 future.apply_1.11.1 codetools_0.2-19 [51] pan_1.9 glue_1.7.0 vcd_1.4-12 vctrs_0.6.5 png_0.1-8 [56] Rdpack_2.6 cellranger_1.1.0 gtable_0.3.4 gower_1.0.1 xfun_0.41 [61] rbibutils_2.2.16 prodlim_2023.08.28 MAPA_2.0.6 pracma_2.4.4 uroot_2.1-3 [66] coda_0.19-4.1timeDate_4032.109hardhat_1.3.1 lava_1.7.3 statmod_1.5.0 [71] TH.data_1.1-2ipred_0.9-14 xts_0.13.1 rstan_2.32.6 tensorA_0.36.2.1 [76] rpart_4.1.21 nnet_7.3-19 tidyselect_1.2.0 emmeans_1.10.0 compiler_4.3.2 [81] curl_5.2.0 ahead_0.10.0 htmlTable_2.4.2 posterior_1.5.0 checkmate_2.3.1 [86] DEoptimR_1.1-3 fracdiff_1.5-2 quadprog_1.5-8 tfruns_1.5.1 digest_0.6.34 [91] minqa_1.2.6 rmarkdown_2.25 htmltools_0.5.7 pkgconfig_2.0.3 base64enc_0.1-3 [96] lme4_1.1-35.1lhs_1.1.6fastmap_1.1.1 rlang_1.1.3 htmlwidgets_1.6.4 [101] quantmod_0.4.26 farver_2.1.1 jsonlite_1.8.8 ModelMetrics_1.2.2.2 R.oo_1.26.0 [106] Formula_1.2-5bayesplot_1.11.1 texreg_1.39.3 GPfit_1.0-8 munsell_0.5.0 [111] furrr_0.3.1 stringi_1.8.3pROC_1.18.5 pkgbuild_1.4.3 plyr_1.8.9 [116] expint_0.1-8 listenv_0.9.1splines_4.3.2 hms_1.1.3ranger_0.16.0 [121] rngtools_1.5.2 reshape2_1.4.4 rstantools_2.4.0 evaluate_0.23
Re: [R] Issues with R's forecast function
Thanks for the kind feedback. I will go ahead and update the packages and see what happens. I will keep you posted. Cheers, Paul El lun, 27 may 2024 a las 13:51, Sarah Goslee () escribió: > Hi Paul, > > Looking at this, you aren't running the most recent version of forecast. > > If I were having a problem of this sort, I'd update R (if you can), > run update.packages() and then try again with a minimal set of > packages. As one of the other responses suggested, you probably have > mismatched versions of packages with dependencies. > > Sarah > > On Mon, May 27, 2024 at 2:48 PM Paul Bernal > wrote: > > > > Dear Sarah, > > > > Here is the sessionInfo() output, I forgot to include it in my reply. > > > > sessionInfo() > > R version 4.3.2 (2023-10-31 ucrt) > > Platform: x86_64-w64-mingw32/x64 (64-bit) > > Running under: Windows 11 x64 (build 22631) > > > > Matrix products: default > > > > > > locale: > > [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United > States.utf8 > > [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C > > [5] LC_TIME=English_United States.utf8 > > > > time zone: America/Bogota > > tzcode source: internal > > > > attached base packages: > > [1] parallel grid stats4stats graphics grDevices utils >datasets methods base > > > > other attached packages: > > [1] mvgam_1.1.1insight_0.19.7 > marginaleffects_0.20.1 brms_2.21.0 > > [5] mgcv_1.9-0 nlme_3.1-163 gbm_2.1.9 > yardstick_1.3.1 > > [9] workflowsets_1.1.0 workflows_1.1.4tune_1.2.1 > rsample_1.2.1 > > [13] recipes_1.0.10 parsnip_1.2.1 modeldata_1.3.0 > infer_1.0.7 > > [17] dials_1.2.1scales_1.3.0 broom_1.0.5 > tidymodels_1.2.0 > > [21] ggthemes_5.1.0 janitor_2.2.0 tictoc_1.2.1 > Ckmeans.1d.dp_4.3.5 > > [25] magrittr_2.0.3 data.table_1.14.10 reticulate_1.34.0 > tensorflow_2.15.0 > > [29] keras_2.13.0 matlabr_1.5.2 R.matlab_3.7.0 > distrMod_2.9.1 > > [33] RandVar_1.2.3 distrEx_2.9.2 distr_2.9.3 > sfsmisc_1.1-17 > > [37] startupmsg_0.9.6.1 qcc_2.7pdp_0.8.1 > doParallel_1.0.17 > > [41] iterators_1.0.14 foreach_1.5.2 tsintermittent_1.10 > ivreg_0.6-2 > > [45] vars_1.6-0 urca_1.3-3 strucchange_1.5-3 > Amelia_1.8.1 > > [49] Rcpp_1.0.12VIM_6.2.2 colorspace_2.1-0 > mi_1.1 > > [53] Hmisc_5.1-1missForest_1.5 mice_3.16.0 > gghighlight_0.4.1 > > [57] caret_6.0-94 lattice_0.21-9 xgboost_1.7.7.1 > smooth_4.0.0 > > [61] e1071_1.7-14 greybox_2.0.0 rio_1.0.1 > fitdistrplus_1.1-11 > > [65] AER_1.2-12 survival_3.5-7 sandwich_3.1-0 > lmtest_0.9-40 > > [69] zoo_1.8-12 car_3.1-2 carData_3.0-5 > forcats_1.0.0 > > [73] stringr_1.5.1 purrr_1.0.2readr_2.1.5 > tidyr_1.3.1 > > [77] tibble_3.2.1 tidyverse_2.0.0dplyr_1.1.4 > Metrics_0.1.4 > > [81] corrgram_1.14 corrplot_0.92 readxl_1.4.3 > glmnet_4.1-8 > > [85] Matrix_1.6-1.1 MASS_7.3-60.0.1actuar_3.3-4 > neuralnet_1.44.2 > > [89] nnfor_0.9.9generics_0.1.3 ggplot2_3.5.1 > lubridate_1.9.3 > > [93] tseries_0.10-55forecast_8.21.1 > > > > loaded via a namespace (and not attached): > > [1] matrixStats_1.3.0DiceDesign_1.10 httr_1.4.7 > RColorBrewer_1.1-3 tools_4.3.2 > > [6] doRNG_1.8.6 backports_1.4.1 utf8_1.2.4 > R6_2.5.1 jomo_2.7-6 > > [11] withr_3.0.0 sp_2.1-3 Brobdingnag_1.2-9 > gridExtra_2.3cli_3.6.2 > > [16] labeling_0.4.3 tsutils_0.9.4mvtnorm_1.2-4 > robustbase_0.99-2randomForest_4.7-1.1 > > [21] proxy_0.4-27 QuickJSR_1.1.3 StanHeaders_2.32.7 > foreign_0.8-85 R.utils_2.12.3 > > [26] parallelly_1.36.0scoringRules_1.1.1 itertools_0.1-3 > TTR_0.24.4 rstudioapi_0.16.0 > > [31] shape_1.4.6 distributional_0.4.0 inline_0.3.19 > loo_2.7.0fansi_1.0.6 > > [36] abind_1.4-5 R.methodsS3_1.8.2lifecycle_1.0.4 > multcomp_1.4-25 whisker_0.4.1 > > [41] snakecase_0.11.1 crayon_1.5.2 mitml_0.4-5 > zeallot_0.1.0pillar_1.9.0 > > [46] knitr_1.45 boot_1.3-28.1estimability_1.4.1 > future.apply_1.11.1 codetools_0.2-19 > > [51] pan_1.9 glue_1.7.0 vcd_1.4-12 > vctrs_0.6.5 png_0.1-8 > > [56] Rdpack_2.6 cellranger_1.1.0 gtable_0.3.4 > gower_1.0.1 xfun_0.41 > > [61] rbibutils_2.2.16 prodlim_2023.08.28 MAPA_2.0.6 > pracma_2.4.4 uroot_2.1-3 > > [66] coda_0.19-4.1timeDate_4032.109hardhat_1.3.1 > lava_1.7.3 statmod_1.5.0 > > [71] TH.data_1.1-2ipred_0.9-14 xts_0.13.1 > rstan_2.32.6 tensorA_0.36.2.1 > > [76]
Re: [R] Issues with R's forecast function
Dear Sarah, Thank you for kindly reaching back. I did load the package, which makes this issue really odd. I ran the same model about a week ago and everything was working to perfection. Best regards, Paul El lun, 27 may 2024 a las 12:15, Sarah Goslee () escribió: > Hi Paul, > > It looks like you're using the forecast package, right? Have you loaded it? > > What is the output of sessionInfo() ? > > It looks to me like you either haven't loaded the needed packages, or > there's some kind of conflict. Your examples don't give me errors when > I run them, so we need more information. > > Sarah > > > > On Mon, May 27, 2024 at 12:25 PM Paul Bernal > wrote: > > > > Dear all, > > > > I am currently using R 4.3.2 and the data I am working with is the > > following: > > > > ts_ingresos_reservas= ts(ingresos_reservaciones$RESERVACIONES, start > = > > c(1996,11), end = c(2024,4), frequency = 12) > > > > structure(c(11421.54, 388965.46, 254774.78, 228066.02, 254330.44, > > 272561.38, 377802.1, 322810.02, 490996.48, 581998.3, 557009.96, > > 619568.56, 578893.9, 938765.36, 566374.38, 582678.46, 931035.04, > > 855661.3, 839760.22, 745521.4, 816424.96, 899616.64, 921462.88, > > 942825, 1145845.74, 1260554.36, 1003983.5, 855516.22, 1273913.68, > > 1204626.54, 1034135.18, 904641.14, 1003094.3, 1073084.74, 928515.64, > > 854864.4, 928927.48, 1076922.34, 1031265.04, 1043755.7, 1238565.12, > > 1343609.54, 1405817.92, 1243192.86, 1235505.44, 1280514.56, 1314029.08, > > 1562841.28, 1405662.96, 1315083.12, 1363980.02, 1126195.72, 1542338.98, > > 1577437.94, 1474855.98, 1287170.56, 1404118.3, 1528979.66, 1286690.34, > > 1544495.16, 1527018.22, 1462908.72, 1682739.76, 1439027.72, 1531060.44, > > 1793606.88, 1835054.26, 1616743.96, 1779745.24, 1772628, 1736200.18, > > 1736792.72, 1835714.4, 2031238.04, 1937816.14, 1942473.52, 2131666.68, > > 2099279.26, 1939093.78, 2135231.54, 2187614.52, 2150766.28, 2179862.62, > > 2467330.32, 2421603.34, 2585889.54, 4489381.11, 4915745.55, 5313521.43, > > 5185438.48, 5346116.46, 4507418.33, 5028489.81, 4931266.16, 5529189.46, > > 5470279.34, 5354912.01, 5937028.11, 6422819.13, 5989941.72, 6549070.26, > > 6710738.34, 6745949.78, 6345832.78, 6656868.36, 6836903.51, 6456545.14, > > 7039815.42, 7288665.89, 7372047.96, 8116822.48, 7318300.42, 8742429.72, > > 8780764.44, 8984081.22, 8221966.77, 8594896.69, 8319125.91, 8027227.8, > > 9241082.48, 8765799.78, 9360643.68, 9384937.59, 8237007.99, 9251122.07, > > 8703017.5, 9004464.9, 8099029.39, 8883214.99, 8360815.05, 8408082.51, > > 9126756.64, 8610501.05, 9109139.05, 8904803.6, 12766215.9, 14055014.03, > > 12789865.86, 13251587.21, 13731917.7, 14925330.72, 14295954.4, > > 13346681.84, 14233732.03, 12743141.34, 13742979.78, 11770238.46, > > 11655300, 12327000, 10096000, 8712000, 6742500, 7199000, 5459000, > > 4442000, 7448500, 6322500, 6030500, 5521000, 4752000, 6248500, > > 5233000, 7440500, 5604500, 6516500, 6001500, 9364500, 14528500, > > 14076000, 11671500, 11778500, 13902500, 13073000, 11097000, 9547500, > > 10255000, 8986500, 10807000, 10031500, 9847000, 12216500, 11648500, > > 13106000, 10856500, 9679500, 9986500, 8947500, 11105500, 9950500, > > 10922000, 9031500, 9720500, 9709000, 9470500, 9316000, 9884500, > > 9067500, 8985000, 10888000, 9676500, 10047000, 8952000, 10191500, > > 12763000, 14885000, 13592000, 13364500, 11924000, 13888000, 12833500, > > 12239000, 945, 10028000, 10171500, 13648000, 13989000, 14488000, > > 14195000, 12800500, 12703000, 1530, 14963000, 15049000, 13513000, > > 14155500, 14047500, 12923500, 13298500, 12814000, 13492000, 14405500, > > 12597500, 14486000, 12103500, 12815000, 11912000, 12353500, 12718500, > > 12972000, 12499000, 13683500, 17437000, 18147000, 17008000, 1718, > > 1616, 15096500, 13707000, 16254000, 14673500, 13661500, 17014000, > > 16104500, 17113000, 17200500, 15304500, 17131000, 16551000, 16356000, > > 14702000, 14488000, 14902500, 14435500, 15598500, 14754500, 15015000, > > 16444500, 1462, 15701000, 14211000, 15243000, 13898000, 14889000, > > 18571000, 15950500, 20171000, 20096000, 19647000, 20394500, 18213000, > > 18714500, 18301000, 14581000, 12333000, 14482500, 17538500, 17480500, > > 19574000, 18464500, 1941, 19013000, 16523500, 18755000, 18194000, > > 18918000, 34130500, 34421500, 36727000, 33406500, 34779500, 35916500, > > 36193000, 35878500, 32274500, 35097000, 34319500, 36459000, 35222500, > > 35972000, 37382000, 34482000, 35776000, 3533, 3599, 34788500, > > 32173500, 34879000, 33195500, 35243500, 33581000, 35632000, 32716000, > > 33966500, 31778000, 28164500, 25729500, 23034500, 24427500, 26506500, > > 26655500), tsp = c(1996.833, 2024.25, 12), class = "ts") > > > > Now that I have my time series data, I tried generating forecasts with > the > > following code: > > > > ingresos_reservas_arimamod = auto.arima(ts_ingresos_reservas) > > ingresos_reservas_arimafor = forecast(ingresos_reservas_arimamod, h > = > > 151) > > > > ingresos_reservas
Re: [R] Issues with R's forecast function
You have completely ignored mentioning what R contributed packages you may have been using in "back when it worked". It is critical that you keep track of which "library" statements are necessary to run your code, if any. I searched for "R usemethod forecast" in Google and this [1] came up. Perhaps it is helpful? It seems that some people have had problems when they updated some but not all of their R packages. [1] https://stackoverflow.com/questions/70283794/forecasting-in-r-usemethod-model-function-error On May 27, 2024 9:24:50 AM PDT, Paul Bernal wrote: >Dear all, > >I am currently using R 4.3.2 and the data I am working with is the >following: > >ts_ingresos_reservas= ts(ingresos_reservaciones$RESERVACIONES, start = >c(1996,11), end = c(2024,4), frequency = 12) > >structure(c(11421.54, 388965.46, 254774.78, 228066.02, 254330.44, >272561.38, 377802.1, 322810.02, 490996.48, 581998.3, 557009.96, >619568.56, 578893.9, 938765.36, 566374.38, 582678.46, 931035.04, >855661.3, 839760.22, 745521.4, 816424.96, 899616.64, 921462.88, >942825, 1145845.74, 1260554.36, 1003983.5, 855516.22, 1273913.68, >1204626.54, 1034135.18, 904641.14, 1003094.3, 1073084.74, 928515.64, >854864.4, 928927.48, 1076922.34, 1031265.04, 1043755.7, 1238565.12, >1343609.54, 1405817.92, 1243192.86, 1235505.44, 1280514.56, 1314029.08, >1562841.28, 1405662.96, 1315083.12, 1363980.02, 1126195.72, 1542338.98, >1577437.94, 1474855.98, 1287170.56, 1404118.3, 1528979.66, 1286690.34, >1544495.16, 1527018.22, 1462908.72, 1682739.76, 1439027.72, 1531060.44, >1793606.88, 1835054.26, 1616743.96, 1779745.24, 1772628, 1736200.18, >1736792.72, 1835714.4, 2031238.04, 1937816.14, 1942473.52, 2131666.68, >2099279.26, 1939093.78, 2135231.54, 2187614.52, 2150766.28, 2179862.62, >2467330.32, 2421603.34, 2585889.54, 4489381.11, 4915745.55, 5313521.43, >5185438.48, 5346116.46, 4507418.33, 5028489.81, 4931266.16, 5529189.46, >5470279.34, 5354912.01, 5937028.11, 6422819.13, 5989941.72, 6549070.26, >6710738.34, 6745949.78, 6345832.78, 6656868.36, 6836903.51, 6456545.14, >7039815.42, 7288665.89, 7372047.96, 8116822.48, 7318300.42, 8742429.72, >8780764.44, 8984081.22, 8221966.77, 8594896.69, 8319125.91, 8027227.8, >9241082.48, 8765799.78, 9360643.68, 9384937.59, 8237007.99, 9251122.07, >8703017.5, 9004464.9, 8099029.39, 8883214.99, 8360815.05, 8408082.51, >9126756.64, 8610501.05, 9109139.05, 8904803.6, 12766215.9, 14055014.03, >12789865.86, 13251587.21, 13731917.7, 14925330.72, 14295954.4, >13346681.84, 14233732.03, 12743141.34, 13742979.78, 11770238.46, >11655300, 12327000, 10096000, 8712000, 6742500, 7199000, 5459000, >4442000, 7448500, 6322500, 6030500, 5521000, 4752000, 6248500, >5233000, 7440500, 5604500, 6516500, 6001500, 9364500, 14528500, >14076000, 11671500, 11778500, 13902500, 13073000, 11097000, 9547500, >10255000, 8986500, 10807000, 10031500, 9847000, 12216500, 11648500, >13106000, 10856500, 9679500, 9986500, 8947500, 11105500, 9950500, >10922000, 9031500, 9720500, 9709000, 9470500, 9316000, 9884500, >9067500, 8985000, 10888000, 9676500, 10047000, 8952000, 10191500, >12763000, 14885000, 13592000, 13364500, 11924000, 13888000, 12833500, >12239000, 945, 10028000, 10171500, 13648000, 13989000, 14488000, >14195000, 12800500, 12703000, 1530, 14963000, 15049000, 13513000, >14155500, 14047500, 12923500, 13298500, 12814000, 13492000, 14405500, >12597500, 14486000, 12103500, 12815000, 11912000, 12353500, 12718500, >12972000, 12499000, 13683500, 17437000, 18147000, 17008000, 1718, >1616, 15096500, 13707000, 16254000, 14673500, 13661500, 17014000, >16104500, 17113000, 17200500, 15304500, 17131000, 16551000, 16356000, >14702000, 14488000, 14902500, 14435500, 15598500, 14754500, 15015000, >16444500, 1462, 15701000, 14211000, 15243000, 13898000, 14889000, >18571000, 15950500, 20171000, 20096000, 19647000, 20394500, 18213000, >18714500, 18301000, 14581000, 12333000, 14482500, 17538500, 17480500, >19574000, 18464500, 1941, 19013000, 16523500, 18755000, 18194000, >18918000, 34130500, 34421500, 36727000, 33406500, 34779500, 35916500, >36193000, 35878500, 32274500, 35097000, 34319500, 36459000, 35222500, >35972000, 37382000, 34482000, 35776000, 3533, 3599, 34788500, >32173500, 34879000, 33195500, 35243500, 33581000, 35632000, 32716000, >33966500, 31778000, 28164500, 25729500, 23034500, 24427500, 26506500, >26655500), tsp = c(1996.833, 2024.25, 12), class = "ts") > >Now that I have my time series data, I tried generating forecasts with the >following code: > >ingresos_reservas_arimamod = auto.arima(ts_ingresos_reservas) >ingresos_reservas_arimafor = forecast(ingresos_reservas_arimamod, h = >151) > >ingresos_reservas_holtwintersmod = HoltWinters(ts_ingresos_reservas) >ingresos_reservas_holtwintersfor = >forecast(ingresos_reservas_holtwintersmod, h = 151) > >ingresos_reservas_etsmod= ets(ts_ingresos_reservas) >ingresos_reservas_etsfor= forecast(ingresos_reservas_etsmod, level >= c(90,99), h = 151) > >
Re: [R] Issues with R's forecast function
Hi Paul, It looks like you're using the forecast package, right? Have you loaded it? What is the output of sessionInfo() ? It looks to me like you either haven't loaded the needed packages, or there's some kind of conflict. Your examples don't give me errors when I run them, so we need more information. Sarah On Mon, May 27, 2024 at 12:25 PM Paul Bernal wrote: > > Dear all, > > I am currently using R 4.3.2 and the data I am working with is the > following: > > ts_ingresos_reservas= ts(ingresos_reservaciones$RESERVACIONES, start = > c(1996,11), end = c(2024,4), frequency = 12) > > structure(c(11421.54, 388965.46, 254774.78, 228066.02, 254330.44, > 272561.38, 377802.1, 322810.02, 490996.48, 581998.3, 557009.96, > 619568.56, 578893.9, 938765.36, 566374.38, 582678.46, 931035.04, > 855661.3, 839760.22, 745521.4, 816424.96, 899616.64, 921462.88, > 942825, 1145845.74, 1260554.36, 1003983.5, 855516.22, 1273913.68, > 1204626.54, 1034135.18, 904641.14, 1003094.3, 1073084.74, 928515.64, > 854864.4, 928927.48, 1076922.34, 1031265.04, 1043755.7, 1238565.12, > 1343609.54, 1405817.92, 1243192.86, 1235505.44, 1280514.56, 1314029.08, > 1562841.28, 1405662.96, 1315083.12, 1363980.02, 1126195.72, 1542338.98, > 1577437.94, 1474855.98, 1287170.56, 1404118.3, 1528979.66, 1286690.34, > 1544495.16, 1527018.22, 1462908.72, 1682739.76, 1439027.72, 1531060.44, > 1793606.88, 1835054.26, 1616743.96, 1779745.24, 1772628, 1736200.18, > 1736792.72, 1835714.4, 2031238.04, 1937816.14, 1942473.52, 2131666.68, > 2099279.26, 1939093.78, 2135231.54, 2187614.52, 2150766.28, 2179862.62, > 2467330.32, 2421603.34, 2585889.54, 4489381.11, 4915745.55, 5313521.43, > 5185438.48, 5346116.46, 4507418.33, 5028489.81, 4931266.16, 5529189.46, > 5470279.34, 5354912.01, 5937028.11, 6422819.13, 5989941.72, 6549070.26, > 6710738.34, 6745949.78, 6345832.78, 6656868.36, 6836903.51, 6456545.14, > 7039815.42, 7288665.89, 7372047.96, 8116822.48, 7318300.42, 8742429.72, > 8780764.44, 8984081.22, 8221966.77, 8594896.69, 8319125.91, 8027227.8, > 9241082.48, 8765799.78, 9360643.68, 9384937.59, 8237007.99, 9251122.07, > 8703017.5, 9004464.9, 8099029.39, 8883214.99, 8360815.05, 8408082.51, > 9126756.64, 8610501.05, 9109139.05, 8904803.6, 12766215.9, 14055014.03, > 12789865.86, 13251587.21, 13731917.7, 14925330.72, 14295954.4, > 13346681.84, 14233732.03, 12743141.34, 13742979.78, 11770238.46, > 11655300, 12327000, 10096000, 8712000, 6742500, 7199000, 5459000, > 4442000, 7448500, 6322500, 6030500, 5521000, 4752000, 6248500, > 5233000, 7440500, 5604500, 6516500, 6001500, 9364500, 14528500, > 14076000, 11671500, 11778500, 13902500, 13073000, 11097000, 9547500, > 10255000, 8986500, 10807000, 10031500, 9847000, 12216500, 11648500, > 13106000, 10856500, 9679500, 9986500, 8947500, 11105500, 9950500, > 10922000, 9031500, 9720500, 9709000, 9470500, 9316000, 9884500, > 9067500, 8985000, 10888000, 9676500, 10047000, 8952000, 10191500, > 12763000, 14885000, 13592000, 13364500, 11924000, 13888000, 12833500, > 12239000, 945, 10028000, 10171500, 13648000, 13989000, 14488000, > 14195000, 12800500, 12703000, 1530, 14963000, 15049000, 13513000, > 14155500, 14047500, 12923500, 13298500, 12814000, 13492000, 14405500, > 12597500, 14486000, 12103500, 12815000, 11912000, 12353500, 12718500, > 12972000, 12499000, 13683500, 17437000, 18147000, 17008000, 1718, > 1616, 15096500, 13707000, 16254000, 14673500, 13661500, 17014000, > 16104500, 17113000, 17200500, 15304500, 17131000, 16551000, 16356000, > 14702000, 14488000, 14902500, 14435500, 15598500, 14754500, 15015000, > 16444500, 1462, 15701000, 14211000, 15243000, 13898000, 14889000, > 18571000, 15950500, 20171000, 20096000, 19647000, 20394500, 18213000, > 18714500, 18301000, 14581000, 12333000, 14482500, 17538500, 17480500, > 19574000, 18464500, 1941, 19013000, 16523500, 18755000, 18194000, > 18918000, 34130500, 34421500, 36727000, 33406500, 34779500, 35916500, > 36193000, 35878500, 32274500, 35097000, 34319500, 36459000, 35222500, > 35972000, 37382000, 34482000, 35776000, 3533, 3599, 34788500, > 32173500, 34879000, 33195500, 35243500, 33581000, 35632000, 32716000, > 33966500, 31778000, 28164500, 25729500, 23034500, 24427500, 26506500, > 26655500), tsp = c(1996.833, 2024.25, 12), class = "ts") > > Now that I have my time series data, I tried generating forecasts with the > following code: > > ingresos_reservas_arimamod = auto.arima(ts_ingresos_reservas) > ingresos_reservas_arimafor = forecast(ingresos_reservas_arimamod, h = > 151) > > ingresos_reservas_holtwintersmod = HoltWinters(ts_ingresos_reservas) > ingresos_reservas_holtwintersfor = > forecast(ingresos_reservas_holtwintersmod, h = 151) > > ingresos_reservas_etsmod= ets(ts_ingresos_reservas) > ingresos_reservas_etsfor= forecast(ingresos_reservas_etsmod, level > = c(90,99), h = 151) > > ingresos_reservas_batsmod = bats(ts_ingresos_reservas) > ingresos_reservas_batsfor = forecast(ingresos_reservas_batsmo
