Timeline for Why is K-fold cross validation bad for time series? [duplicate]
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Sep 11, 2017 at 21:15 | comment | added | Rob Hyndman | See robjhyndman.com/publications/cv-time-series | |
| Sep 11, 2017 at 18:46 | history | closed |
Kodiologist Richard Hardy jbowman kjetil b halvorsen♦ Michael R. Chernick |
Duplicate of Cross-validation techniques for time series data | |
| Sep 11, 2017 at 18:39 | review | Close votes | |||
| Sep 11, 2017 at 18:46 | |||||
| Sep 11, 2017 at 18:13 | comment | added | elemolotiv | @RichardHardy great link! let's mark it as duplicate, thanks :) | |
| Sep 11, 2017 at 17:41 | comment | added | Richard Hardy | See this answer. Let me know if your question should be marked duplicate of the other one. | |
| Sep 11, 2017 at 17:23 | comment | added | elemolotiv | ok but that would make CV more pessimistic, but that's not too bad. The problem is usually the opposite ... being too optimistic. besides K-Fold is no based on splitting the data random, rather in K chunks :) | |
| Sep 11, 2017 at 17:18 | comment | added | meh | CV works on random splitting of the data, so among (many other things) it's not clear that you would be able to split the data so that seasonality is preserved. | |
| Sep 11, 2017 at 17:14 | history | edited | elemolotiv | CC BY-SA 3.0 |
edited title
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| Sep 11, 2017 at 17:08 | history | asked | elemolotiv | CC BY-SA 3.0 |