  Many time series data mining problems can be solved with repeated use of
distance measure. Examples of such tasks include similarity search, clustering,
classification, anomaly detection and segmentation. For over two decades it has
been known that the Dynamic Time Warping (DTW) distance measure is the best
measure to use for most tasks, in most domains. Because the classic DTW
algorithm has quadratic time complexity, many ideas have been introduced to
reduce its amortized time, or to quickly approximate it. One of the most cited
approximate approaches is FastDTW. The FastDTW algorithm has well over a
thousand citations and has been explicitly used in several hundred research
efforts. In this work, we make a surprising claim. In any realistic data mining
application, the approximate FastDTW is much slower than the exact DTW. This
fact clearly has implications for the community that uses this algorithm:
allowing it to address much larger datasets, get exact results, and do so in
less time.
