I am trying to forecast the number of incoming calls for the next 4 period. While my forecast shows me the same figure for the next 4 periods, so I am a little confused as to where did I go wrong.
Data:
Time Total Calls
8/1/2015 69676
9/1/2015 71827
10/1/2015 62504
11/1/2015 59431
12/1/2015 63304
1/1/2016 58899
2/1/2016 55922
3/1/2016 60463
4/1/2016 56121
5/1/2016 58574
6/1/2016 64467
7/1/2016 61825
8/1/2016 75784
9/1/2016 67047
10/1/2016 63000
11/1/2016 63318
12/1/2016 66612
1/1/2017 71614
2/1/2017 62875
3/1/2017 66297
4/1/2017 66193
5/1/2017 70143
6/1/2017 72259
7/1/2017 65793
8/1/2017 53687
9/1/2017 48518
10/1/2017 58740
11/1/2017 50801
12/1/2017 44293
1/1/2018 61150
2/1/2018 49619
3/1/2018 49621
4/1/2018 48645
5/1/2018 37958
6/1/2018 37725
7/1/2018 42221
8/1/2018 41663
9/1/2018 35328
10/1/2018 37687
Trying to Forecast the next 4 months data using R
tier2=ts(tier2,start=c(2015,8),end=c(2019,2),frequency=12)
tier2_train<-window(tier2[,2],end=c(2018,10))
tier2_test<-window(tier2[,2],start=c(2018,11))
plot(tier2_train,xlab="Time Period",ylab="Total Calls")
automatic<auto.arima(tier2_train,seasonal=T,stepwise=FALSE,approximation=FALSE,ic="aicc")
# automatic ** The model decided (0,1,1)
forecast1 <- forecast::forecast(automatic, h = 4)
forecast1
Forecast output::
Point Forecast
Nov 2018 37716
Dec 2018 37716
Jan 2019 37716
Feb 2019 37716
37716 for the next 4 months does not seem appropriate. How do I calculate the forecast for the next 4 months
R code mentioned above
Expected results: to be close to:
11/1/2018 31657
12/1/2018 26390
1/1/2019 27542
2/1/2019 23262
your problem is similar to https://stats.stackexchange.com/questions/286900/arima-forecast-straight-line
Basicly auto.arima is indeed searching for seasonal models but it is also searching for non seasonal models, use the argument trace so that you can see the models being tested.
To "solve" this problem refer to the link and force D>1.
example.
plot(forecast(auto.arima(tier2_train,trace = TRUE,seasonal = TRUE,D=1)))
also here is the data
structure(c(32L, 36L, 4L, 8L, 11L, 1L, 14L, 17L, 20L, 23L, 26L,
29L, 33L, 37L, 5L, 9L, 12L, 2L, 15L, 18L, 21L, 24L, 27L, 30L,
34L, 38L, 6L, 10L, 13L, 3L, 16L, 19L, 22L, 25L, 28L, 31L, 35L,
39L, 7L, 32L, 36L, 4L, 8L, 69676L, 71827L, 62504L, 59431L, 63304L,
58899L, 55922L, 60463L, 56121L, 58574L, 64467L, 61825L, 75784L,
67047L, 63000L, 63318L, 66612L, 71614L, 62875L, 66297L, 66193L,
70143L, 72259L, 65793L, 53687L, 48518L, 58740L, 50801L, 44293L,
61150L, 49619L, 49621L, 48645L, 37958L, 37725L, 42221L, 41663L,
35328L, 37687L, 69676L, 71827L, 62504L, 59431L), .Dim = c(43L,
2L), .Dimnames = list(NULL, c("Time", "Total_Calls")), .Tsp = c(2015.58333333333,
2019.08333333333, 12), class = c("mts", "ts", "matrix"))
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