Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle
  Inequality and Point Clustering   Dynamic time warping (DTW) plays an important role in analytics on time
series. Despite the large body of research on speeding up univariate DTW, the
method for multivariate DTW has not been improved much in the last two decades.
The most popular algorithm used today is still the one developed seventeen
years ago. This paper presents a solution that, as far as we know, for the
first time consistently outperforms the classic multivariate DTW algorithm
across dataset sizes, series lengths, data dimensions, temporal window sizes,
and machines. The new solution, named TC-DTW, introduces Triangle Inequality
and Point Clustering into the algorithm design on lower bound calculations for
multivariate DTW. In experiments on DTW-based nearest neighbor finding, the new
solution avoids as much as 98% (60% average) DTW distance calculations and
yields as much as 25X (7.5X average) speedups.

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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
30
The categories of this paper are: q-alg