What type of classification algorithm for recognizing events in a set of data?
You will need your data to look something like this:

Blue Red Green Label
20   12  13    _
18   11  13    _
18   12  13    _
19   13  14    _
24  12   13    _
28  14   19    B
30  19   21    B
29  18   20    B
25  14   16    B
21  12   13    _
19  11   12    _
18  11   12    _


That is, every point in the data will need to be annotated with a label: _ for nothing, B for blue, Y for yellow. 

A sequence labeling model can be trained to predict the label for each point in the input sequence. Note that it's common to use a "Begin Inside Outside" (BIO) scheme to help the model understand when an "event" starts and ends:

Blue Red Green Label
20   12  13    Outside
18   11  13    Outside
18   12  13    Outside
19   13  14    Outside
24  12   13    Outside
28  14   19    B_Begin
30  19   21    B_Inside
29  18   20    B_Inside
25  14   16    B_Inside
21  12   13    Outside
19  11   12    Outside
18  11   12    Outside


(there are also variants of this scheme)

As far as I know Conditional Random Fields would be the traditional approach for this kind of task, but there might more recent NN approaches that I'm not aware of.