Timeline for What is the natural progression from discrete AR models into continuous time?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Feb 8 at 11:59 | history | edited | Richard Hardy | CC BY-SA 4.0 |
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| Feb 8 at 11:57 | comment | added | Richard Hardy | @MilTom, there you go :) | |
| Feb 8 at 11:17 | comment | added | MilTom | My bad, I was not clear enough. Yes, I want to predict daily up to 30 days ahead. Different models for varying prediction horizon makes sense | |
| Feb 8 at 11:11 | comment | added | Richard Hardy | @MilTom, I did not understand that you want to predict 1, 2, ..., 30 days ahead. You wrote 30, so I understood it literally, or perhaps "in a narrow sense". You can build separate models for different prediction horizons. This is not uncommon. E.g. the model for predicting 1 step ahead could very well include the first lag (and more, if needed). | |
| Feb 8 at 10:54 | comment | added | MilTom | I understand your point, however if we assume that current time is $t$ and if our regressors are available up to $t$, then when we shift them by 30 lags, to predict tomorrow $t+1$, we are using regressor values up to 29 days ago and the other 29 days are reserved for predicting $t+2$ until $t+30$. This means that predicting tomorrow will not use regressor data from yesterday which is probably the most relevant one. Unless I am missing something here? | |
| Feb 8 at 8:31 | history | answered | Richard Hardy | CC BY-SA 4.0 |