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= ";")
you are getting same forecast value because your data is white noise (no trend, season and cycle), if the model find such data it will simply take avg and print same result for all forecast, therefore you are getting same forecast.
By the way i think you didn't create model ex. arima. You should first build model then forecast.