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]]
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
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 <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.-- Sent from my phone. Please excuse my brevity.