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beckyperriment authored Dec 7, 2023
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We compared our approach with two other DTW clustering packages, \texttt{DTAIDistance} [@Meert2020Dtaidistance] and \texttt{TSlearn} [@Tavenard2020TslearnData]. The datasets used for the comparison are from the UCR Time Series Classification Archive [@Dau2018TheArchive], and consist of 128 time series datasets with up to 16,800 data series of lengths up to 2,844. The full results can be found in the Appendix. Benchmarking against \texttt{TSlearn} was stopped after the first 22 datasets because the results were consistently over 20 times slower than \texttt{DTW-C++}. \autoref{tab} shows the results for datasets downselected to have a number of time series ($N$) greater than 100 and a length of each time series greater than 500 points. This is because \texttt{DTW-C++} is aimed at larger datasets where the speed improvements are more relevant.

<font size= "1">Table: Computational time comparison of \texttt{DTW-C++} using MIP and k-medoids, vs.\ \texttt{DTAIDistance}, and \texttt{TSlearn}, on datasets in the UCR Time Series Classification Archive where $N>100$ and $L>500$. \label{tab}
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Table: Computational time comparison of \texttt{DTW-C++} using MIP and k-medoids, vs.\ \texttt{DTAIDistance}, and \texttt{TSlearn}, on datasets in the UCR Time Series Classification Archive where $N>100$ and $L>500$. \label{tab}

| | Number of time series | Length of time series | DTW-C++ MIP (s) | DTW-C++ k-Medoids (s) | DTAI Distance (s) | Time decrease (%) |
|----------------------------|-----------------------|-----------------------|-----------------|-----------------------|-------------------|--------------------|
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| StarLightCurves | 8236 | 1024 | N/A | **18551.7** | 27558.1 | 33 |
| UWaveGestureLibraryAll | 3582 | 945 | N/A | **1194.6** | 4436.9 | 73 |

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# Acknowledgements

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