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 <paulberna...@gmail.com> 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 stats4 stats graphics grDevices utils
> datasets methods base
>
> other attached packages:
> [1] mvgam_1.1.1 insight_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_1.1.4 tune_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_1.2.1 scales_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_2.7 pdp_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_1.0.12 VIM_6.2.2 colorspace_2.1-0
> mi_1.1
> [53] Hmisc_5.1-1 missForest_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_1.0.2 readr_2.1.5
> tidyr_1.3.1
> [77] tibble_3.2.1 tidyverse_2.0.0 dplyr_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_7.3-60.0.1 actuar_3.3-4
> neuralnet_1.44.2
> [89] nnfor_0.9.9 generics_0.1.3 ggplot2_3.5.1
> lubridate_1.9.3
> [93] tseries_0.10-55 forecast_8.21.1
>
> loaded via a namespace (and not attached):
> [1] matrixStats_1.3.0 DiceDesign_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_2.3 cli_3.6.2
> [16] labeling_0.4.3 tsutils_0.9.4 mvtnorm_1.2-4
> robustbase_0.99-2 randomForest_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_1.36.0 scoringRules_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_2.7.0 fansi_1.0.6
> [36] abind_1.4-5 R.methodsS3_1.8.2 lifecycle_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_0.1.0 pillar_1.9.0
> [46] knitr_1.45 boot_1.3-28.1 estimability_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_0.19-4.1 timeDate_4032.109 hardhat_1.3.1
> lava_1.7.3 statmod_1.5.0
> [71] TH.data_1.1-2 ipred_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_1.1-35.1 lhs_1.1.6 fastmap_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_1.2-5 bayesplot_1.11.1 texreg_1.39.3
> GPfit_1.0-8 munsell_0.5.0
> [111] furrr_0.3.1 stringi_1.8.3 pROC_1.18.5
> pkgbuild_1.4.3 plyr_1.8.9
> [116] expint_0.1-8 listenv_0.9.1 splines_4.3.2
> hms_1.1.3 ranger_0.16.0
> [121] rngtools_1.5.2 reshape2_1.4.4 rstantools_2.4.0
> evaluate_0.23 RcppParallel_5.1.7
> [126] laeken_0.5.3 nloptr_2.0.3 tzdb_0.4.0
> future_1.33.1 xtable_1.8-4
> [131] class_7.3-22 snow_0.4-4 arm_1.13-1
> cluster_2.1.4 timechange_0.2.0
> [136] globals_0.16.2 bridgesampling_1.1-2
>
> Cheers,
>
> Paul
>
> El lun, 27 may 2024 a las 12:15, Sarah Goslee (<sarah.gos...@gmail.com>)
> 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 <paulberna...@gmail.com> 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, 9450000, 10028000, 10171500, 13648000, 13989000, 14488000,
>> > 14195000, 12800500, 12703000, 15300000, 14963000, 15049000, 13513000,
>> > 14155500, 14047500, 12923500, 13298500, 12814000, 13492000, 14405500,
>> > 12597500, 14486000, 12103500, 12815000, 11912000, 12353500, 12718500,
>> > 12972000, 12499000, 13683500, 17437000, 18147000, 17008000, 17180000,
>> > 16160000, 15096500, 13707000, 16254000, 14673500, 13661500, 17014000,
>> > 16104500, 17113000, 17200500, 15304500, 17131000, 16551000, 16356000,
>> > 14702000, 14488000, 14902500, 14435500, 15598500, 14754500, 15015000,
>> > 16444500, 14620000, 15701000, 14211000, 15243000, 13898000, 14889000,
>> > 18571000, 15950500, 20171000, 20096000, 19647000, 20394500, 18213000,
>> > 18714500, 18301000, 14581000, 12333000, 14482500, 17538500, 17480500,
>> > 19574000, 18464500, 19410000, 19013000, 16523500, 18755000, 18194000,
>> > 18918000, 34130500, 34421500, 36727000, 33406500, 34779500, 35916500,
>> > 36193000, 35878500, 32274500, 35097000, 34319500, 36459000, 35222500,
>> > 35972000, 37382000, 34482000, 35776000, 35330000, 35990000, 34788500,
>> > 32173500, 34879000, 33195500, 35243500, 33581000, 35632000, 32716000,
>> > 33966500, 31778000, 28164500, 25729500, 23034500, 24427500, 26506500,
>> > 26655500), tsp = c(1996.83333333333, 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_batsmod, level
>> > = c(90,99), h = 151, robust = TRUE)
>> >
>> > ingresos_reservas_tbatsmod = tbats(ts_ingresos_reservas)
>> > ingresos_reservas_tbatsfor = forecast(ingresos_reservas_tbatsmod,
>> > level = c(90,99), h = 151, robust = TRUE)
>> >
>> > ingresos_reservas_nnetarmod = nnetar(ts_ingresos_reservas)
>> > ingresos_reservas_nnetarfor = forecast(ingresos_reservas_nnetarmod,
>> > PI = TRUE, h = 151, robust = TRUE)
>> >
>> > This code used to work, but now, I keep getting the following error:
>> > Error in UseMethod("forecast", object) :
>> > no applicable method for 'forecast' applied to an object of class "ets"
>> >
>> > Error in UseMethod("forecast", object) :
>> > no applicable method for 'forecast' applied to an object of class
>> > "nnetar"
>> >
>> > Error in UseMethod("forecast", object) :
>> > no applicable method for 'forecast' applied to an object of class "bats"
>> >
>> > Error in UseMethod("forecast", object) :
>> > no applicable method for 'forecast' applied to an object of class "bats"
>> >
>> > It seems like the forecast function is not working for these models
>> > anymore. Any idea of how to solve this issue?
>> >
>> > Kind regards,
>> >
>> > Paul
>> >
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