Timeline for Structuring experiment/training data with months in mind
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Dec 1 at 1:06 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Oct 5, 2021 at 2:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| May 18, 2021 at 4:03 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Mar 17, 2021 at 13:52 | answer | added | Brian Spiering | timeline score: 0 | |
| Feb 12, 2020 at 12:40 | comment | added | lte__ | Yeah but it's not just dependent on month... And by the time I do PCA etc, I end up with 750 features, and then it's still influenced by other variables... I just want to try this, I do have alternative approaches in mind. I do want to focus on what I'm asking in this question in this thread. | |
| Feb 12, 2020 at 12:33 | comment | added | zina | can you detect features influencing results and make them dependent on months? than you can have influence of previous months + impact of a month you are going to predict | |
| Feb 12, 2020 at 10:23 | comment | added | lte__ | Not yet, might look into it, problem is that the data is not just impacted by month, but also our colleagues' strategy, and time series will not capture that. I'm thinking I'd somehow do a kernel density matching? But no idea how to transfer the density once I model it. | |
| Feb 12, 2020 at 10:05 | comment | added | zina | Hi, did you think about using time series analysis () considering as well seasonal effects?smth like SARIMA? just an idea.. | |
| Feb 12, 2020 at 8:35 | history | asked | lte__ | CC BY-SA 4.0 |