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fpetitjean-DBA-73d7789\cython\build\temp.linux-x86_64-3.6\DBA.o (2154728, 2019-01-18)
fpetitjean-DBA-73d7789\cython\build.sh (45, 2019-01-18)
fpetitjean-DBA-73d7789\cython\DBA.c (1077564, 2019-01-18)
fpetitjean-DBA-73d7789\cython\DBA.cpython-36m-x86_64-linux-gnu.so (891968, 2019-01-18)
fpetitjean-DBA-73d7789\cython\DBA.pyx (7926, 2019-01-18)
fpetitjean-DBA-73d7789\cython\setup.py (229, 2019-01-18)
fpetitjean-DBA-73d7789\cython\test.py (1196, 2019-01-18)
fpetitjean-DBA-73d7789\DBA.java (7384, 2019-01-18)
fpetitjean-DBA-73d7789\DBA.m (4208, 2019-05-01)
fpetitjean-DBA-73d7789\DBA.py (6274, 2019-01-18)
fpetitjean-DBA-73d7789\DBAWarpingWindow.java (7901, 2019-01-18)
fpetitjean-DBA-73d7789\DBA_multivariate.py (7329, 2019-01-18)
fpetitjean-DBA-73d7789\images\arithmetic.png (248511, 2019-01-18)
fpetitjean-DBA-73d7789\images\DBA.png (254753, 2019-01-18)
fpetitjean-DBA-73d7789\LICENSE (35120, 2019-01-18)
fpetitjean-DBA-73d7789\cython\build\temp.linux-x86_64-3.6 (0, 2019-01-18)
fpetitjean-DBA-73d7789\cython\build (0, 2019-01-18)
fpetitjean-DBA-73d7789\cython (0, 2019-01-18)
fpetitjean-DBA-73d7789\images (0, 2019-01-18)
fpetitjean-DBA-73d7789 (0, 2019-01-18)

DBA === DBA stands for Dynamic Time Warping Barycenter Averaging. DBA is an averaging method that is consistent with Dynamic Time Warping. I give below an example of the difference between the traditional arithmetic mean of the set of time series and DBA.   # Underlying research and scientific papers This code is supporting 3 research papers: * [Pattern Recognition 2011](http://francois-petitjean.com/Research/Petitjean2011-PR.pdf): A global averaging method for Dynamic Time Warping * [ICDM 2014](http://francois-petitjean.com/Research/Petitjean2014-ICDM-DTW.pdf): Dynamic Time Warping Averaging of Time Series allows Faster and more Accurate Classification * [ICDM 2017](http://francois-petitjean.com/Research/ForestierPetitjean2017-ICDM.pdf): Generating synthetic time series to augment sparse datasets When using this repository, please cite: ``` @ARTICLE{Petitjean2011-DBA, title={A global averaging method for dynamic time warping, with applications to clustering}, author={Petitjean, Fran{\c{c}}ois and Ketterlin, Alain and Gan{\c{c}}arski, Pierre}, journal={Pattern Recognition}, volume={44}, number={3}, pages={678--693}, year={2011}, publisher={Elsevier} } @INPROCEEDINGS{Petitjean2014-ICDM-2, title={Dynamic time warping averaging of time series allows faster and more accurate classification}, author={Petitjean, Fran{\c{c}}ois and Forestier, Germain and Webb, Geoffrey I and Nicholson, Ann E and Chen, Yanping and Keogh, Eamonn}, booktitle={Data Mining (ICDM), 2014 IEEE International Conference on}, pages={470--479}, year={2014}, organization={IEEE} } @INPROCEEDINGS{Forestier2017-ICDM, title={Generating synthetic time series to augment sparse datasets}, author={Forestier, Germain and Petitjean, Fran{\c{c}}ois and Dau, Hoang Anh and Webb, Geoffrey I and Keogh, Eamonn}, booktitle={Data Mining (ICDM), 2017 IEEE International Conference on}, pages={865--870}, year={2017}, organization={IEEE} } ``` # Organisation of the repository This repository gives you different versions of DBA for different programming language, whether you want to have a warping window or not, etc. Apologies for the inconsistencies between versions but I've basically created them as the need arose. Each file corresponds to one of these combinations; if one is missing for your usage, let me know. Currently the length is limited to 1,000 (let me know if you need more). * `DBA.java` standard DBA in Java with no warping window and memory allocated statically * `DBAWarpingWindow.java` same as `DBA.java` but with a warping window as a parameter * `DBA.m` Matlab implementation of DBA with no windows * `DBA.py` Fast Python implementation of DBA with no windows * `DBA_multivariate.py` Fast Python implementation of DBA for multi-variate time series with no windows * `cython/*` Cython implementation (thus usable in Python) of DBA with warping window (mono-variate)

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