Original project: https://code.google.com/p/fastdtw/
FastDTW is an approximate Dynamic Time Warping (DTW) algorithm that provides optimal or near-optimal alignments with an O(N) time and memory complexity, in contrast to the O(N^2) requirement for the standard DTW algorithm. FastDTW uses a multilevel approach that recursively projects a solution from a coarser resolution and refines the projected solution.
FastDTW is implemented in Java. If the JVM heap size is not large enough for the cost matrix to fit into memory, the implementation will automatically switch to an on-disk cost matrix. Alternate approaches evaluated in the papers listed below are also implemented: Sakoe-Chiba Band, Abstraction, Piecewise Dynamic Time Warping (PDTW).
This is the original/official implementation used in the experiments described in the papers below.
FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space. Stan Salvador & Philip Chan. KDD Workshop on Mining Temporal and Sequential Data, pp. 70-80, 2004. (http://cs.fit.edu/~pkc/papers/tdm04.pdf)
Toward accurate dynamic time wrapping in linear time and space. Stan Salvador and Philip Chan. Intelligent Data Analysis, 11(5):561-580, 2007. (http://iospress.metapress.com/content/h74w3l3156332247)