Timeline for Recognizing the Seasonal Effects from a Time Plot
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 16, 2021 at 13:34 | comment | added | Chesso | Here is the answer to my previous question: otexts.com/fpp2/seasonal-arima.html | |
| Mar 16, 2021 at 13:29 | comment | added | Richard Hardy | I think the answer should mention the glaring problem of overdifferencing, i.e. differencing data that does not contain unit roots but contains deterministic trends. You have addressed differencing but not in this way. | |
| Mar 16, 2021 at 13:15 | comment | added | Chesso | I have completed this task, however It produces a notation I have not seen before. What is meant by ARIMA(0,1,2)(0,1,1)[12]? If this should be asked in the R section of stackoverflow, please let me know. | |
| Mar 16, 2021 at 10:43 | comment | added | Stephan Kolassa | Significance only tells you about in-sample fit. If that is all you want, go ahead, but usually people want to forecast their time series, and then in-sample fit gets misleading very quickly. I would really recommend relying on auto.arima() as to differencing - it's very hard to beat. | |
| Mar 16, 2021 at 10:36 | comment | added | Chesso | Understood. I have fitted my initial plot, and have found a cubic model to be highly significant. So in the case of differencing, should I not be using a third order difference? Use a lower order difference? Not use differencing at all? | |
| Mar 16, 2021 at 10:34 | vote | accept | Chesso | ||
| Mar 16, 2021 at 7:37 | history | answered | Stephan Kolassa | CC BY-SA 4.0 |