Timeline for Make daily business data stationary for ARIMA
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 13, 2019 at 5:23 | vote | accept | Patrick Glettig | ||
| May 12, 2019 at 9:19 | comment | added | IrishStat | In summary, you would now NOT NEED an ARMA model or any differencing as the residuals FROM A causative model ARE approximately WHITE NOISE. The suggested causative model includes mean shifts and other temporal variables such as before and after 4 holidays ,day-of-the-week, month-of-the-year,. day-of-the-month , monday-after-a-holiday | |
| May 12, 2019 at 6:53 | comment | added | IrishStat | essentially yes or simply include two indicator series (level shifts) as predictors. Detecting level shifts can sometimes (not in this case however) be done with the R program tsoutliers cran.r-project.org/web/packages/tsoutliers/tsoutliers.pdf or AUTOBOX ( which is available in R) and which I have helped to write. In general the technnique is called Intervention Detection docplayer.net/… | |
| May 12, 2019 at 6:10 | comment | added | Patrick Glettig | Wow, thank you so much! There is plenty of interesting insights from this. I would like to ask two things for clarification: by demeaning the data, do you simply mean subtracting the average for periods in the 3 different levels? What technique can I use to discover the two model shifts? | |
| May 11, 2019 at 20:27 | history | edited | IrishStat | CC BY-SA 4.0 |
added 124 characters in body
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| May 11, 2019 at 20:17 | history | answered | IrishStat | CC BY-SA 4.0 |