  Learning a good distance measure for distance-based classification in time
series leads to significant performance improvement in many tasks.
Specifically, it is critical to effectively deal with variations and temporal
dependencies in time series. However, existing metric learning approaches focus
on tackling variations mainly using a strict alignment of two sequences,
thereby being not able to capture temporal dependencies. To overcome this
limitation, we propose MLAT, which covers both alignment and temporal
dependencies at the same time. MLAT achieves the alignment effect as well as
preserves temporal dependencies by augmenting a given time series using a
sliding window. Furthermore, MLAT employs time-invariant metric learning to
derive the most appropriate distance measure from the augmented samples which
can also capture the temporal dependencies among them well. We show that MLAT
outperforms other existing algorithms in the extensive experiments on various
real-world data sets.
