Classification of streaming time series under more realistic assumptions

Much of the vast literature on time series classification makes several assumptions about data and the algorithm's eventual deployment that are almost certainly unwarranted. For example, many research efforts assume that the beginning and ending points of the pattern of interest can be correctly identified, during both the training phase and later deployment. Another example is the common assumption that queries will be made at a constant rate...