Timeline for How does temporal data leakage happen?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Nov 8 at 18:02 | comment | added | yang | the point is "feature" comparing to target never use future information. For example: input(d1-d7):target(d8), input(d8-d14):target(d15), input(d3-d9):target(d10), etc. There is no leakage if you check any single data point. However, if I assign input(d1-d7):target(d8), input(d8-d14):target(d15) in training set, and input(d3-d9):target(d10) in testing set, people start to say there is data leakage since I have "future data" in training compare to testing. Why? | |
| Nov 8 at 17:45 | comment | added | Stephan Kolassa | I think I stll do not understand what exactly you mean by "aggregate". Whether your features are engineered using only "past" information is irrelevant. If during training, you use features that contain "future" information, or use "future" actuals (which your model may use as "future" features), you have leakage. | |
| Nov 8 at 17:11 | comment | added | yang | keep in mind that I assume training data is strictly prepared with historical price as input and future price as target. In your credit example, it is equivalent to for each training instance, you can only use user's history events to predict future events. For each instance, there is no data leakage. My question is about why when aggregate these no leakage instances, there is data leakage? | |
| Nov 8 at 16:17 | history | answered | Stephan Kolassa | CC BY-SA 4.0 |