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I'm trying to perform anomaly detection on the open data from citibike. They are giving bikeshare trips for the past 30+ months, as well as monthly reports. In those reports they say how many bikes have been repaired each month.

The samples I am building are a sample by day and by bike. I actually don't have label for those samples, since I don't know which bike has been repaired which day. But I know that by classifying each sample normal or anormal, I can sum the number of bikes that have been classified anormal during a month and compare that number to the monthly report.

I want to know how one usually deal with it, or how this is called so I can read research paper on the subject.

Exemple of samples :

bikeid, day,         feature1, feature2...
1,      2016-01-01,  0.6,      -0.2 
2,      2016-01-01,  0.5,      -0.8
1,      2016-01-02,  0.7,      -0.1
2,      2016-01-02,  0.9,       1
   ...
1,      2016-01-31, -0.32,     -0.45
2,      2016-01-31, -0.5,      -0.8

Example of label: 3456 bicycle repairs in January.

But the shape of the data is irrelevant, what is important is that the labels are not about one sample but a group of samples.

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  • $\begingroup$ It's a little unclear to me, can you give some small samples of data? $\endgroup$ Jul 8 '16 at 8:50
  • $\begingroup$ @Jan van der Vegt : I edited to give you samples of data but I don't care about this problem in particular, I just don't know how to describe my problem to Google $\endgroup$
    – Borbag
    Jul 8 '16 at 9:00
  • $\begingroup$ maybe aggregation is the word you are looking for.. $\endgroup$
    – Valentas
    Jul 8 '16 at 16:52
  • $\begingroup$ Aggregate Output Learning was the right therm, thanks @Valentas. $\endgroup$
    – Borbag
    Jul 15 '16 at 9:37
  • $\begingroup$ cs.cmu.edu/~jfolson/documents/musicantd-AggregateLearning.pdf Here is the paper giving the definition $\endgroup$
    – Borbag
    Jul 15 '16 at 9:38

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