Dynamic Time Warping

Dynamic time warping (DTW) algorithm is used for estimation of similarities between two sequences which may fluctate in time or speed. For example, using DTW we can easily detect the walking patterns of one person who was walking with different speeds in different videos, even if there were accelerations or decelerations during the course of one's observations. DTW has variety of practical applications and used for analysis of any data that can be represented in linear fashion including audio, video and graphics. The best example of DTW is coping with different speaking speeds in automatic speech recognization.

Finding recurring patterns in process data, also referred to as motif-matching, may reveal diagnostic information to engineers and operators. Dynamic Time Warping (DTW) is one of the most widely used techniques for performing these motif matches with certain restrictions. The sequences are "warped" non-linearly in the time dimension to determine a measure of their similarity independent of certain non-linear variations in the time dimension. This sequence alignment method is often used in the context of hidden Markov models.

One example of the restrictions imposed on the matching of the sequences is on the monotonicity of the mapping in the time dimension. Continuity is less important in DTW than in other pattern matching algorithms; DTW is an algorithm particularly suited to matching sequences with missing information, provided there are long enough segments for matching to occur. The sample implementation of the algorithm is available on wikipedia.

Classification methods of DNA most commonly use comparison of the differences in DNA symbolic records, which requires the global multiple sequence alignment. This solution is often inappropriate, causing a number of imprecisions and requires additional user intervention for exact alignment of the similar segments. The similar segments in DNA represented as a signal are characterized by a similar shape of the curve. The DNA alignment in genomic signals may adjust whole sections not only individual symbols. The dynamic time warping (DTW) is suitable for this purpose and can replace the multiple alignment of symbolic sequences in applications, such as phylogenetic analysis (Classification of genomic signals using dynamic time warping;
Skutkova et al., 2013).

Classification of genomic signals using dynamic time warping is an adequate variant to phylogenetic analysis using the symbolic DNA sequences alignment; in addition, it is robust, quick and more precise technique.

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