Application of shapelet discovery and shapelet transform on datasets without label

Hello ,
I have a dataset , like this where Q0 is the feature value and TS is the time stamp , and I would like to apply shapelet discovery and shapelet transform on this csv file. I have this huge one time series which I have sliced down to the number of parts(data snippets) , And every snippet is similar to this(below one) , now what I would like to do is to shapelet discovery first and then shapelet transform in order to detect anomalies in the time series data.
Q0 TS
0.012364804744720459, 2018-03-02 00:44:51.303082
0.012344598770141602, 2018-03-02 00:44:51.375207
0.012604951858520508, 2018-03-02 00:44:51.475198
0.012307226657867432, 2018-03-02 00:44:51.575189
0.012397348880767822, 2018-03-02 00:44:51.675180
0.013141036033630371, 2018-03-02 00:44:51.775171
0.012811839580535889, 2018-03-02 00:44:51.875162
0.012950420379638672, 2018-03-02 00:44:51.975153
0.013257980346679688, 2018-03-02 00:44:52.075144

This issue names no file, test, or concrete code change. Start by inspecting the existing shapelet discovery and shapelet transform entry points and the expected dataset shape, using the supplied Q0 and TS columns as the example. Done means establishing whether unlabeled snippets are supported and documenting a reproducible path, or clarifying the feature request if they are not.