Timeline for In a time series forecasting, should we apply differencing on entire dataset if one or two features are non stationary?
Current License: CC BY-SA 4.0
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| Mar 11, 2022 at 10:18 | comment | added | Fatafim | Thank you, I misunderstood the intention. In such case, overdifferencing stationary time series might lead to poor accuracy, instead of that it's worth checking, if simply differencing the nonstationary series and including them in VAR altogether will work. Running several autocorrelation tests is imperative. If that doesn't work, I would suggest using hybrid models or 2 steps approach. It is unclear to me however how the relationships between the series have been determined (e.g. is there any cointegration?), so it's rather difficult to give a straightforward answer. | |
| Mar 11, 2022 at 6:36 | comment | added | Richard Hardy | Clearly, the OP is not considering using VAR with nonstationary time series. They are considering using VAR with overdifferenced time series instead. | |
| S Mar 10, 2022 at 21:31 | review | First answers | |||
| Mar 11, 2022 at 18:48 | |||||
| S Mar 10, 2022 at 21:31 | history | answered | Fatafim | CC BY-SA 4.0 |