The technique of dynamic time warping (DTW) is relied on heavily in isolated word recognition systems. The advantage of using DTW is that reliable time alignment between reference and test patterns is obtained. The disadvantage of using DTW is the heavy computational burden required to find the optimal time alignment path. Several alternative procedures have been proposed for reducing the compu...

Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC [1], we perform a series of experiments to test whether this sta...

Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, DTW, in its original formulation, is extremely inefficient in comparing long sparse time series, which mostly contain zeros and unevenly spaced non-zero observations. Original DTW distance does not take advantage of the sparsity, and thus, incur a prohibitively large comp...

This research proposes the application of dynamic time warping (DTW) algorithm to analyse multivariate data from virtual reality training simulators, to assess the skill level of trainees. We present results of DTW algorithm applied to trajectory data from a virtual reality haptic training simulator for epidural needle insertion. The proposed application of DTW algorithm serves two purposes, to...

Dynamic time warping (DTW) has been widely used in various pattern recognition and time series data mining applications. However, as examples will illustrate, both the classic DTW and its later alternative, derivative DTW, may fail to align a pair of sequences on their common trends or patterns. Furthermore, the learning capability of any supervised learning algorithm based on classic/derivativ...

This paper systematically explores the capabilities of different forms of Dynamic Time Warping (DTW) algorithms and their parameter configurations in recognising whole-of-body gestures. The standard DTW (SDTW) (Sakoe and Chiba 1978), globally feature weighted DTW (Reyes et al. 2011) and locally feature weighted DTW (Arici et al. 2013) algorithms are particularly considered, while an enhanced ve...

Dynamic Time Warping (DTW) is a widely used technique for univariate time series comparison. This paper proposes a new algorithm for the comparison of multivariate time series which generalize DTW for the needs of correlated multivariate time series.

Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot ...

In this contribution we describe a novel classification approach for on-line handwriting recognition. The technique combines dynamic time warping (DTW) and support vector machines (SVMs) by establishing a new SVM kernel. We call this kernel Gaussian DTW (GDTW) kernel. This kernel approach has a main advantage over common HMM techniques. It does not assume a model for the generative class condit...

Dynamic Time Warping (DTW) is certainly the most relevant distance for time series analysis. However, its quadratic time complexity may hamper its use, mainly in the analysis of large time series data. All the recent advances in speeding up the exact DTW calculation are confined to similarity search. However, there is a significant number of important algorithms including clustering and classif...