Timeline for Handling large gaps of missing data in the dataset
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 9, 2017 at 0:27 | answer | added | Steffen Moritz | timeline score: 0 | |
| Jun 1, 2017 at 21:46 | comment | added | whuber♦ | I have run into trouble with missing meteorological data, because they can be absent for important reasons, such as discovery that a gauge had been failing or was destroyed in a storm and then was replaced. It therefore looks risky to assume you can impute the values as if these gap periods had been randomly chosen. Moreover, sometimes such gaps are the only warning you get that some important change has occurred in the data series. It can pay, therefore, to begin by comparing the data just before a gap to the data just after, whether or not you intend to impute any of the missing data. | |
| Jun 1, 2017 at 20:25 | answer | added | Eben | timeline score: 0 | |
| May 7, 2017 at 7:17 | answer | added | Björn | timeline score: 1 | |
| May 7, 2017 at 3:37 | history | asked | ace_01S | CC BY-SA 3.0 |