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,      -0.81,      2016-01-02,  0.7,      -0.12,      2016-01-02,  0.9,       1   ...1,      2016-01-31, -0.32,     -0.452,      2016-01-31, -0.5,      -0.8Example 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. 
how to learn from unlabeled samples but labeled group of samples?
classification;anomaly detection;labels