Timeline for How to create forecast data prediction interval bands
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 5, 2013 at 19:35 | vote | accept | Adrian | ||
| Nov 5, 2013 at 19:35 | vote | accept | Adrian | ||
| Nov 5, 2013 at 19:35 | |||||
| Nov 5, 2013 at 17:15 | answer | added | user31668 | timeline score: 1 | |
| Nov 5, 2013 at 16:30 | comment | added | Adrian | @Eupraxis1981 yes the linear trend is fine. | |
| Nov 4, 2013 at 21:42 | comment | added | user31668 | Thanks for clarifying. I was asking about the specific data you are modeling. Is there a reason to believe that the linear trend model is appropriate. I.e., would you expect "regression towards the mean" at time $t>t_0$ if you saw a large/small value at $t_0$? Or, would you expect the trend to continue from the last observed point? | |
| Nov 4, 2013 at 21:27 | comment | added | Adrian | @Eupraxis1981 The data is from google analytics so I am guessing it is deterministic in time. BTW my stats background is minimal. | |
| Nov 4, 2013 at 19:25 | comment | added | user31668 | Are you sure that your time series is deterministic in time? I.e., $Y_t=at+\epsilon_t$ after deseasonalizing, or have you considered a random walk with fixed drift: $Y_{t+1}=Y_t+\delta+\epsilon_t$? Where $\delta$ is a fixed "drift".The differnece is that the second form has much more variance and will not fall on a neat line, but it will increase over time. Just something to consider, as that info will help us recommend prediction bands. | |
| Nov 4, 2013 at 18:03 | review | First posts | |||
| Nov 4, 2013 at 18:15 | |||||
| Nov 4, 2013 at 17:47 | history | asked | Adrian | CC BY-SA 3.0 |