R time series forecast is always the same











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I have a lot of time series and i want a forecast for every single one for ten months. For some of them it works, for the most i always get the same forecast for every month.
The time series consists monthly data. For example:



> ts(Menge[Nummer==8 & Jahr>2014 & Index<61 ], frequency=12)

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1   6 225   0 114  21  25   5 256   1   6   1   8

2  13  35 180 215  20  48  20  31 283 130   3   1

3  53  31   0 142  60  76  10  28 298  29   5  14


The output of dput is:



dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ])

c(6, 225, 0, 114, 21, 25, 5, 256, 1, 6, 1, 8, 13, 35, 180, 215, 

20, 48, 20, 31, 283, 130, 3, 1, 53, 31, 0, 142, 60, 76, 10, 28, 

298, 29, 5, 14)


When i decompose the time series i get seasonality and a trend:



> decompose(ts(Menge[Nummer==8 & Jahr>2014 & Index<61 ], frequency=12))

$x

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1   6 225   0 114  21  25   5 256   1   6   1   8

2  13  35 180 215  20  48  20  31 283 130   3   1

3  53  31   0 142  60  76  10  28 298  29   5  14



$seasonal

         Jan        Feb        Mar        Apr        May        Jun        Jul        Aug        Sep        Oct

1 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

2 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

3 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

         Nov        Dec

1 -63.100694 -62.475694

2 -63.100694 -62.475694

3 -63.100694 -62.475694



$trend

       Jan      Feb      Mar      Apr      May      Jun      Jul      Aug      Sep      Oct      Nov      Dec

1       NA       NA       NA       NA       NA       NA 55.95833 48.33333 47.91667 59.62500 63.79167 64.70833

2 66.29167 57.54167 59.91667 76.83333 82.08333 81.87500 83.25000 84.75000 77.08333 66.54167 65.16667 68.00000

3 68.75000 68.20833 68.70833 65.12500 61.00000 61.62500       NA       NA       NA       NA       NA       NA



$random

           Jan          Feb          Mar          Apr          May          Jun          Jul          Aug

1           NA           NA           NA           NA           NA           NA    6.7673611  131.3298611

2  -18.1493056    7.9548611   95.0173611   31.2673611  -29.9201389  -23.5034722   -5.5243056 -130.0868056

3   19.3923611   -6.7118056  -93.7743056  -30.0243056   31.1631944   24.7465278           NA           NA

           Sep          Oct          Nov          Dec

1 -125.7951389  -57.9201389    0.3090278    5.7673611

2  127.0381944   59.1631944    0.9340278   -4.5243056

3           NA           NA           NA           NA



$figure

 [1] -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

[11] -63.100694 -62.475694



$type

[1] "additive"



attr(,"class")

[1] "decomposed.ts"


But the forecast is always the same:



     Point Forecast     Lo 80   Hi 80     Lo 95    Hi 95

Jan 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Feb 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Mar 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Apr 4       68.41899 -47.17701 184.015 -108.3698 245.2078

May 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Jun 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Jul 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Aug 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Sep 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Oct 4       68.41899 -47.17701 184.015 -108.3698 245.2078


The data are three years (2015-2017). The forecast should be for the first ten months of 2018 (so that i can prove how good is the fitting in reality).



I did about 1000 forecasts (by changing "number" i get another time series) and very often i just got the same values, sometimes the point forecast is the same, but the Lo and Hi values change a little bit, in some cases i get different values for every month.



I observed the data in some cases but can't find a reason, why in some cases the forecasts are the same and in other cases not. Especially because i get saisonality and trends by decomposing the time series.



The whole code is:



  setwd("Z:/Bestellvorschlag/Lagerdrehung") #workspace festlegen

    x= read.csv("Daten Aufbereitet.csv", header=TRUE, sep=";")  #read the data

    attach(x)

    library(forecast)

    Zeilenanzahl<-length(x[,1]) #number of rows

    AnzahlArtikel<-x[Zeilenanzahl,1] #number of articles

    ForecastMatrix<-matrix(0,9*AnzahlArtikel,8) #i want nine forecasts for every article

    #with the columns Nummer, Monat,Forecast, lower80, lower 95, upper 80, upper 95, Menge







    for (i in 1:AnzahlArtikel) { #do it for all numbers; each number is another product



    #extract mean(point forecast), lower und upper bounds

    TS<- ts(Menge[Nummer==i & Jahr>2014 & Index<61 ], frequency=12)

    mean<-unlist(forecast(TS,9)[2])

    upper<-unlist(forecast(TS,9)[5])

    lower<-unlist(forecast(TS,9)[6])



    #write the data in a matrix

    for (j in 1:9) {

      ForecastMatrix[9*(i-1)+j,1]<-i

      ForecastMatrix[9*(i-1)+j,2]<-j

      ForecastMatrix[9*(i-1)+j,3]<-mean[j]

      ForecastMatrix[9*(i-1)+j,4]<-lower[j]

      ForecastMatrix[9*(i-1)+j,5]<-lower[9+j]

      ForecastMatrix[9*(i-1)+j,6]<-upper[j]

      ForecastMatrix[9*(i-1)+j,7]<-upper[9+j]

      ForecastMatrix[9*(i-1)+j,8]<-Menge[Nummer==i & Jahr==2018 & Monat==j] #the real value

    }

    }

#write the data in a .csv

write.table(ForecastMatrix, file = "Forecastmatrix.csv", sep= ";")





r forecasting







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Flo Geys is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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  • Please, post the output of dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ]) in the question. And also post the code you are running to model and forecast.
    – Rui Barradas
    2 days ago















up vote
0
down vote

favorite












I have a lot of time series and i want a forecast for every single one for ten months. For some of them it works, for the most i always get the same forecast for every month.
The time series consists monthly data. For example:



> ts(Menge[Nummer==8 & Jahr>2014 & Index<61 ], frequency=12)

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1   6 225   0 114  21  25   5 256   1   6   1   8

2  13  35 180 215  20  48  20  31 283 130   3   1

3  53  31   0 142  60  76  10  28 298  29   5  14


The output of dput is:



dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ])

c(6, 225, 0, 114, 21, 25, 5, 256, 1, 6, 1, 8, 13, 35, 180, 215, 

20, 48, 20, 31, 283, 130, 3, 1, 53, 31, 0, 142, 60, 76, 10, 28, 

298, 29, 5, 14)


When i decompose the time series i get seasonality and a trend:



> decompose(ts(Menge[Nummer==8 & Jahr>2014 & Index<61 ], frequency=12))

$x

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1   6 225   0 114  21  25   5 256   1   6   1   8

2  13  35 180 215  20  48  20  31 283 130   3   1

3  53  31   0 142  60  76  10  28 298  29   5  14



$seasonal

         Jan        Feb        Mar        Apr        May        Jun        Jul        Aug        Sep        Oct

1 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

2 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

3 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

         Nov        Dec

1 -63.100694 -62.475694

2 -63.100694 -62.475694

3 -63.100694 -62.475694



$trend

       Jan      Feb      Mar      Apr      May      Jun      Jul      Aug      Sep      Oct      Nov      Dec

1       NA       NA       NA       NA       NA       NA 55.95833 48.33333 47.91667 59.62500 63.79167 64.70833

2 66.29167 57.54167 59.91667 76.83333 82.08333 81.87500 83.25000 84.75000 77.08333 66.54167 65.16667 68.00000

3 68.75000 68.20833 68.70833 65.12500 61.00000 61.62500       NA       NA       NA       NA       NA       NA



$random

           Jan          Feb          Mar          Apr          May          Jun          Jul          Aug

1           NA           NA           NA           NA           NA           NA    6.7673611  131.3298611

2  -18.1493056    7.9548611   95.0173611   31.2673611  -29.9201389  -23.5034722   -5.5243056 -130.0868056

3   19.3923611   -6.7118056  -93.7743056  -30.0243056   31.1631944   24.7465278           NA           NA

           Sep          Oct          Nov          Dec

1 -125.7951389  -57.9201389    0.3090278    5.7673611

2  127.0381944   59.1631944    0.9340278   -4.5243056

3           NA           NA           NA           NA



$figure

 [1] -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

[11] -63.100694 -62.475694



$type

[1] "additive"



attr(,"class")

[1] "decomposed.ts"


But the forecast is always the same:



     Point Forecast     Lo 80   Hi 80     Lo 95    Hi 95

Jan 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Feb 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Mar 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Apr 4       68.41899 -47.17701 184.015 -108.3698 245.2078

May 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Jun 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Jul 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Aug 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Sep 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Oct 4       68.41899 -47.17701 184.015 -108.3698 245.2078


The data are three years (2015-2017). The forecast should be for the first ten months of 2018 (so that i can prove how good is the fitting in reality).



I did about 1000 forecasts (by changing "number" i get another time series) and very often i just got the same values, sometimes the point forecast is the same, but the Lo and Hi values change a little bit, in some cases i get different values for every month.



I observed the data in some cases but can't find a reason, why in some cases the forecasts are the same and in other cases not. Especially because i get saisonality and trends by decomposing the time series.



The whole code is:



  setwd("Z:/Bestellvorschlag/Lagerdrehung") #workspace festlegen

    x= read.csv("Daten Aufbereitet.csv", header=TRUE, sep=";")  #read the data

    attach(x)

    library(forecast)

    Zeilenanzahl<-length(x[,1]) #number of rows

    AnzahlArtikel<-x[Zeilenanzahl,1] #number of articles

    ForecastMatrix<-matrix(0,9*AnzahlArtikel,8) #i want nine forecasts for every article

    #with the columns Nummer, Monat,Forecast, lower80, lower 95, upper 80, upper 95, Menge







    for (i in 1:AnzahlArtikel) { #do it for all numbers; each number is another product



    #extract mean(point forecast), lower und upper bounds

    TS<- ts(Menge[Nummer==i & Jahr>2014 & Index<61 ], frequency=12)

    mean<-unlist(forecast(TS,9)[2])

    upper<-unlist(forecast(TS,9)[5])

    lower<-unlist(forecast(TS,9)[6])



    #write the data in a matrix

    for (j in 1:9) {

      ForecastMatrix[9*(i-1)+j,1]<-i

      ForecastMatrix[9*(i-1)+j,2]<-j

      ForecastMatrix[9*(i-1)+j,3]<-mean[j]

      ForecastMatrix[9*(i-1)+j,4]<-lower[j]

      ForecastMatrix[9*(i-1)+j,5]<-lower[9+j]

      ForecastMatrix[9*(i-1)+j,6]<-upper[j]

      ForecastMatrix[9*(i-1)+j,7]<-upper[9+j]

      ForecastMatrix[9*(i-1)+j,8]<-Menge[Nummer==i & Jahr==2018 & Monat==j] #the real value

    }

    }

#write the data in a .csv

write.table(ForecastMatrix, file = "Forecastmatrix.csv", sep= ";")





r forecasting







share|improve this question









New contributor




Flo Geys is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.




















  • Please, post the output of dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ]) in the question. And also post the code you are running to model and forecast.
    – Rui Barradas
    2 days ago













up vote
0
down vote

favorite









up vote
0
down vote

favorite











I have a lot of time series and i want a forecast for every single one for ten months. For some of them it works, for the most i always get the same forecast for every month.
The time series consists monthly data. For example:



> ts(Menge[Nummer==8 & Jahr>2014 & Index<61 ], frequency=12)

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1   6 225   0 114  21  25   5 256   1   6   1   8

2  13  35 180 215  20  48  20  31 283 130   3   1

3  53  31   0 142  60  76  10  28 298  29   5  14


The output of dput is:



dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ])

c(6, 225, 0, 114, 21, 25, 5, 256, 1, 6, 1, 8, 13, 35, 180, 215, 

20, 48, 20, 31, 283, 130, 3, 1, 53, 31, 0, 142, 60, 76, 10, 28, 

298, 29, 5, 14)


When i decompose the time series i get seasonality and a trend:



> decompose(ts(Menge[Nummer==8 & Jahr>2014 & Index<61 ], frequency=12))

$x

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1   6 225   0 114  21  25   5 256   1   6   1   8

2  13  35 180 215  20  48  20  31 283 130   3   1

3  53  31   0 142  60  76  10  28 298  29   5  14



$seasonal

         Jan        Feb        Mar        Apr        May        Jun        Jul        Aug        Sep        Oct

1 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

2 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

3 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

         Nov        Dec

1 -63.100694 -62.475694

2 -63.100694 -62.475694

3 -63.100694 -62.475694



$trend

       Jan      Feb      Mar      Apr      May      Jun      Jul      Aug      Sep      Oct      Nov      Dec

1       NA       NA       NA       NA       NA       NA 55.95833 48.33333 47.91667 59.62500 63.79167 64.70833

2 66.29167 57.54167 59.91667 76.83333 82.08333 81.87500 83.25000 84.75000 77.08333 66.54167 65.16667 68.00000

3 68.75000 68.20833 68.70833 65.12500 61.00000 61.62500       NA       NA       NA       NA       NA       NA



$random

           Jan          Feb          Mar          Apr          May          Jun          Jul          Aug

1           NA           NA           NA           NA           NA           NA    6.7673611  131.3298611

2  -18.1493056    7.9548611   95.0173611   31.2673611  -29.9201389  -23.5034722   -5.5243056 -130.0868056

3   19.3923611   -6.7118056  -93.7743056  -30.0243056   31.1631944   24.7465278           NA           NA

           Sep          Oct          Nov          Dec

1 -125.7951389  -57.9201389    0.3090278    5.7673611

2  127.0381944   59.1631944    0.9340278   -4.5243056

3           NA           NA           NA           NA



$figure

 [1] -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

[11] -63.100694 -62.475694



$type

[1] "additive"



attr(,"class")

[1] "decomposed.ts"


But the forecast is always the same:



     Point Forecast     Lo 80   Hi 80     Lo 95    Hi 95

Jan 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Feb 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Mar 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Apr 4       68.41899 -47.17701 184.015 -108.3698 245.2078

May 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Jun 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Jul 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Aug 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Sep 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Oct 4       68.41899 -47.17701 184.015 -108.3698 245.2078


The data are three years (2015-2017). The forecast should be for the first ten months of 2018 (so that i can prove how good is the fitting in reality).



I did about 1000 forecasts (by changing "number" i get another time series) and very often i just got the same values, sometimes the point forecast is the same, but the Lo and Hi values change a little bit, in some cases i get different values for every month.



I observed the data in some cases but can't find a reason, why in some cases the forecasts are the same and in other cases not. Especially because i get saisonality and trends by decomposing the time series.



The whole code is:



  setwd("Z:/Bestellvorschlag/Lagerdrehung") #workspace festlegen

    x= read.csv("Daten Aufbereitet.csv", header=TRUE, sep=";")  #read the data

    attach(x)

    library(forecast)

    Zeilenanzahl<-length(x[,1]) #number of rows

    AnzahlArtikel<-x[Zeilenanzahl,1] #number of articles

    ForecastMatrix<-matrix(0,9*AnzahlArtikel,8) #i want nine forecasts for every article

    #with the columns Nummer, Monat,Forecast, lower80, lower 95, upper 80, upper 95, Menge







    for (i in 1:AnzahlArtikel) { #do it for all numbers; each number is another product



    #extract mean(point forecast), lower und upper bounds

    TS<- ts(Menge[Nummer==i & Jahr>2014 & Index<61 ], frequency=12)

    mean<-unlist(forecast(TS,9)[2])

    upper<-unlist(forecast(TS,9)[5])

    lower<-unlist(forecast(TS,9)[6])



    #write the data in a matrix

    for (j in 1:9) {

      ForecastMatrix[9*(i-1)+j,1]<-i

      ForecastMatrix[9*(i-1)+j,2]<-j

      ForecastMatrix[9*(i-1)+j,3]<-mean[j]

      ForecastMatrix[9*(i-1)+j,4]<-lower[j]

      ForecastMatrix[9*(i-1)+j,5]<-lower[9+j]

      ForecastMatrix[9*(i-1)+j,6]<-upper[j]

      ForecastMatrix[9*(i-1)+j,7]<-upper[9+j]

      ForecastMatrix[9*(i-1)+j,8]<-Menge[Nummer==i & Jahr==2018 & Monat==j] #the real value

    }

    }

#write the data in a .csv

write.table(ForecastMatrix, file = "Forecastmatrix.csv", sep= ";")





r forecasting







share|improve this question









New contributor




Flo Geys is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











I have a lot of time series and i want a forecast for every single one for ten months. For some of them it works, for the most i always get the same forecast for every month.
The time series consists monthly data. For example:



> ts(Menge[Nummer==8 & Jahr>2014 & Index<61 ], frequency=12)

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1   6 225   0 114  21  25   5 256   1   6   1   8

2  13  35 180 215  20  48  20  31 283 130   3   1

3  53  31   0 142  60  76  10  28 298  29   5  14


The output of dput is:



dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ])

c(6, 225, 0, 114, 21, 25, 5, 256, 1, 6, 1, 8, 13, 35, 180, 215, 

20, 48, 20, 31, 283, 130, 3, 1, 53, 31, 0, 142, 60, 76, 10, 28, 

298, 29, 5, 14)


When i decompose the time series i get seasonality and a trend:



> decompose(ts(Menge[Nummer==8 & Jahr>2014 & Index<61 ], frequency=12))

$x

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1   6 225   0 114  21  25   5 256   1   6   1   8

2  13  35 180 215  20  48  20  31 283 130   3   1

3  53  31   0 142  60  76  10  28 298  29   5  14



$seasonal

         Jan        Feb        Mar        Apr        May        Jun        Jul        Aug        Sep        Oct

1 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

2 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

3 -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

         Nov        Dec

1 -63.100694 -62.475694

2 -63.100694 -62.475694

3 -63.100694 -62.475694



$trend

       Jan      Feb      Mar      Apr      May      Jun      Jul      Aug      Sep      Oct      Nov      Dec

1       NA       NA       NA       NA       NA       NA 55.95833 48.33333 47.91667 59.62500 63.79167 64.70833

2 66.29167 57.54167 59.91667 76.83333 82.08333 81.87500 83.25000 84.75000 77.08333 66.54167 65.16667 68.00000

3 68.75000 68.20833 68.70833 65.12500 61.00000 61.62500       NA       NA       NA       NA       NA       NA



$random

           Jan          Feb          Mar          Apr          May          Jun          Jul          Aug

1           NA           NA           NA           NA           NA           NA    6.7673611  131.3298611

2  -18.1493056    7.9548611   95.0173611   31.2673611  -29.9201389  -23.5034722   -5.5243056 -130.0868056

3   19.3923611   -6.7118056  -93.7743056  -30.0243056   31.1631944   24.7465278           NA           NA

           Sep          Oct          Nov          Dec

1 -125.7951389  -57.9201389    0.3090278    5.7673611

2  127.0381944   59.1631944    0.9340278   -4.5243056

3           NA           NA           NA           NA



$figure

 [1] -35.142361 -30.496528  25.065972 106.899306 -32.163194 -10.371528 -57.725694  76.336806  78.878472   4.295139

[11] -63.100694 -62.475694



$type

[1] "additive"



attr(,"class")

[1] "decomposed.ts"


But the forecast is always the same:



     Point Forecast     Lo 80   Hi 80     Lo 95    Hi 95

Jan 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Feb 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Mar 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Apr 4       68.41899 -47.17701 184.015 -108.3698 245.2078

May 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Jun 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Jul 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Aug 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Sep 4       68.41899 -47.17701 184.015 -108.3698 245.2078

Oct 4       68.41899 -47.17701 184.015 -108.3698 245.2078


The data are three years (2015-2017). The forecast should be for the first ten months of 2018 (so that i can prove how good is the fitting in reality).



I did about 1000 forecasts (by changing "number" i get another time series) and very often i just got the same values, sometimes the point forecast is the same, but the Lo and Hi values change a little bit, in some cases i get different values for every month.



I observed the data in some cases but can't find a reason, why in some cases the forecasts are the same and in other cases not. Especially because i get saisonality and trends by decomposing the time series.



The whole code is:



  setwd("Z:/Bestellvorschlag/Lagerdrehung") #workspace festlegen

    x= read.csv("Daten Aufbereitet.csv", header=TRUE, sep=";")  #read the data

    attach(x)

    library(forecast)

    Zeilenanzahl<-length(x[,1]) #number of rows

    AnzahlArtikel<-x[Zeilenanzahl,1] #number of articles

    ForecastMatrix<-matrix(0,9*AnzahlArtikel,8) #i want nine forecasts for every article

    #with the columns Nummer, Monat,Forecast, lower80, lower 95, upper 80, upper 95, Menge







    for (i in 1:AnzahlArtikel) { #do it for all numbers; each number is another product



    #extract mean(point forecast), lower und upper bounds

    TS<- ts(Menge[Nummer==i & Jahr>2014 & Index<61 ], frequency=12)

    mean<-unlist(forecast(TS,9)[2])

    upper<-unlist(forecast(TS,9)[5])

    lower<-unlist(forecast(TS,9)[6])



    #write the data in a matrix

    for (j in 1:9) {

      ForecastMatrix[9*(i-1)+j,1]<-i

      ForecastMatrix[9*(i-1)+j,2]<-j

      ForecastMatrix[9*(i-1)+j,3]<-mean[j]

      ForecastMatrix[9*(i-1)+j,4]<-lower[j]

      ForecastMatrix[9*(i-1)+j,5]<-lower[9+j]

      ForecastMatrix[9*(i-1)+j,6]<-upper[j]

      ForecastMatrix[9*(i-1)+j,7]<-upper[9+j]

      ForecastMatrix[9*(i-1)+j,8]<-Menge[Nummer==i & Jahr==2018 & Monat==j] #the real value

    }

    }

#write the data in a .csv

write.table(ForecastMatrix, file = "Forecastmatrix.csv", sep= ";")





r forecasting




r forecasting






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  • Please, post the output of dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ]) in the question. And also post the code you are running to model and forecast.
    – Rui Barradas
    2 days ago


















  • Please, post the output of dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ]) in the question. And also post the code you are running to model and forecast.
    – Rui Barradas
    2 days ago
















Please, post the output of dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ]) in the question. And also post the code you are running to model and forecast.
– Rui Barradas
2 days ago




Please, post the output of dput(Menge[Nummer==8 & Jahr>2014 & Index<61 ]) in the question. And also post the code you are running to model and forecast.
– Rui Barradas
2 days ago

















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