  Since its introduction two decades ago, there has been increasing interest in
the problem of early classification of time series. This problem generalizes
classic time series classification to ask if we can classify a time series
subsequence with sufficient accuracy and confidence after seeing only some
prefix of a target pattern. The idea is that the earlier classification would
allow us to take immediate action, in a domain in which some practical
interventions are possible. For example, that intervention might be sounding an
alarm or applying the brakes in an automobile. In this work, we make a
surprising claim. In spite of the fact that there are dozens of papers on early
classification of time series, it is not clear that any of them could ever work
in a real-world setting. The problem is not with the algorithms per se but with
the vague and underspecified problem description. Essentially all algorithms
make implicit and unwarranted assumptions about the problem that will ensure
that they will be plagued by false positives and false negatives even if their
results suggested that they could obtain near-perfect results. We will explain
our findings with novel insights and experiments and offer recommendations to
the community.
