  Early time series classification (eTSC) is the problem of classifying a time
series after as few measurements as possible with the highest possible
accuracy. The most critical issue of any eTSC method is to decide when enough
data of a time series has been seen to take a decision: Waiting for more data
points usually makes the classification problem easier but delays the time in
which a classification is made; in contrast, earlier classification has to cope
with less input data, often leading to inferior accuracy. The state-of-the-art
eTSC methods compute a fixed optimal decision time assuming that every times
series has the same defined start time (like turning on a machine). However, in
many real-life applications measurements start at arbitrary times (like
measuring heartbeats of a patient), implying that the best time for taking a
decision varies heavily between time series. We present TEASER, a novel
algorithm that models eTSC as a two two-tier classification problem: In the
first tier, a classifier periodically assesses the incoming time series to
compute class probabilities. However, these class probabilities are only used
as output label if a second-tier classifier decides that the predicted label is
reliable enough, which can happen after a different number of measurements. In
an evaluation using 45 benchmark datasets, TEASER is two to three times earlier
at predictions than its competitors while reaching the same or an even higher
classification accuracy. We further show TEASER's superior performance using
real-life use cases, namely energy monitoring, and gait detection.
