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 <paulbernal07 at 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 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.-- Sarah Goslee (she/her) https://www.numberwright.com
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 (<sarah.goslee at gmail.com>) escribi?:> 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 <paulbernal07 at 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 > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > -- > Sarah Goslee (she/her) > https://www.numberwright.com >[[alternative HTML version deleted]]
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.goslee at gmail.com>) escribi?:> 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 <paulbernal07 at 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 > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > -- > Sarah Goslee (she/her) > https://www.numberwright.com >[[alternative HTML version deleted]]