Timeline for How to define train and test sets in financial time series for estimating machine learning parameters
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
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| S Aug 10, 2015 at 8:40 | history | suggested | Dawny33 | CC BY-SA 3.0 |
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| Aug 10, 2015 at 7:25 | review | Suggested edits | |||
| S Aug 10, 2015 at 8:40 | |||||
| Jul 2, 2014 at 3:32 | answer | added | Steve S | timeline score: 3 | |
| Mar 18, 2014 at 15:18 | comment | added | Eitan | Thanks alto. Actually, thats exctly what i did but wasn't sure about it. Can the same approach be used for feature selection as well? | |
| Mar 18, 2014 at 15:15 | comment | added | Eitan | @Memming, you may consider an entire daily data of an asset (say 20 years). The data is highly correlated and up/down trend prediction. Thanks! | |
| Mar 18, 2014 at 2:17 | comment | added | alto | Obviously just splitting the data randomly as you normally would in supervised ML is a bad idea for time series. I've used essentially the same approach described at the end of this blog post successfully in the past, but with a step size larger than 1 to save computing time. The paper linked at the bottom was also helpful. | |
| Mar 17, 2014 at 20:16 | comment | added | Memming | The answer would depend on how much data you have, how many things are you trying to estimate, how correlated your data is, and how nonstationary the data might be. | |
| Mar 17, 2014 at 17:31 | answer | added | kris | timeline score: 0 | |
| Mar 16, 2014 at 16:54 | history | edited | Nick Stauner | CC BY-SA 3.0 |
punctuation, capitalization, numeric list formatting
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| Mar 16, 2014 at 16:52 | review | First posts | |||
| Mar 16, 2014 at 16:55 | |||||
| Mar 16, 2014 at 16:33 | history | asked | Eitan | CC BY-SA 3.0 |