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19 changes: 17 additions & 2 deletions README.md
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# DTW-C++
DTW-C++
===========================
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DTW-C++ is a dynamic time warping (DTW) based clustering library in C++. The user can input multiple time series (potentially of variable lengths) and the number of desired clusters if known or a range of possible cluster numbers if not known. DTW-C++ can cluster the time series using k-Medoids or mixed integer programming (MIP). k-Medoids is generally quicker but may be subject to sticking in local optima, whereas MIP can find globally optimal clusters. ANY DEPENDANCIES?

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In this `readme.md` a summary is given. You may find the detailed documentation [here](https://Battery-Intelligence-Lab.github.io/dtw-cpp/).
If you are affected by the sudden change of main branch, please switch to [dtw-cpp_v0.0.2]([https://github.com/Battery-Intelligence-Lab/dtw-cpp/tree/dtw-cpp_v2](https://github.com/Battery-Intelligence-Lab/dtw-cpp/tree/dtwc_0_0_2)) branch.

Introduction
===========================
DTW-C++ is a dynamic time warping (DTW) and clustering library, written in C++, for time series data. The user can input multiple time series (potentially of variable lengths), and the number of desired clusters (if known), or a range of possible cluster numbers (if the specific number is not known). DTW-C++ can cluster time series data using k-medoids or mixed integer programming (MIP); k-medoids is generally quicker, but may be subject getting stuck in local optima, whereas MIP can find globally optimal clusters.
223 changes: 70 additions & 153 deletions joss/paper.bib
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@article{reniers2019review,
title={Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries},
author={Reniers, Jorn M and Mulder, Grietus and Howey, David A},
journal={Journal of The Electrochemical Society},
Doi = {10.1149/2.0281914jes},
volume={166},
number={14},
pages={A3189},
year={2019},
publisher={IOP Publishing}
}

@article{reiners2022digital,
title={Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis},
author={Reniers, Jorn M and Howey, David A},
year={2022},
}


@article{kumtepeli2020energy,
title={Energy arbitrage optimization with battery storage: 3D-MILP for electro-thermal performance and semi-empirical aging models},
author={Kumtepeli, Volkan and Hesse, Holger C and Schimpe, Michael and Tripathi, Anshuman and Wang, Youyi and Jossen, Andreas},
journal={IEEE Access},
volume={8},
pages={204325--204341},
year={2020},
publisher={IEEE}
}

@inproceedings{naumann2017simses,
title={Simses: Software for techno-economic simulation of stationary energy storage systems},
author={Naumann, Maik and Truong, Cong Nam and Schimpe, Michael and Kucevic, Daniel and Jossen, Andreas and Hesse, Holger C},
booktitle={International ETG Congress 2017},
pages={1--6},
year={2017},
organization={VDE}
}

@article{moller2022simses,
title={SimSES: A holistic simulation framework for modeling and analyzing stationary energy storage systems},
author={M{\"o}ller, Marc and Kucevic, Daniel and Collath, Nils and Parlikar, Anupam and Dotzauer, Petra and Tepe, Benedikt and Englberger, Stefan and Jossen, Andreas and Hesse, Holger},
journal={Journal of Energy Storage},
volume={49},
pages={103743},
year={2022},
publisher={Elsevier}
}

@article{tranter2022liionpack,
title={liionpack: A Python package for simulating packs of batteries with PyBaMM},
author={Tranter, Thomas and Timms, Robert and Sulzer, Valentin and Planella, Ferran and Wiggins, Gavin and Karra, Suryanarayana and Agarwal, Priyanshu and Chopra, Saransh and Allu, Srikanth and Shearing, Paul and others},
journal={Journal of Open Source Software},
volume={7},
number={70},
year={2022},
publisher={The Open Journal}
}

@article{howey2019tools,
title={Tools for battery health diagnostics and prediction},
author={Howey, David A},
journal={The Electrochemical Society Interface},
volume={28},
number={1},
pages={55},
year={2019},
publisher={IOP Publishing}
}

@article{Pearson:2017,
Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170304627P},
Archiveprefix = {arXiv},
Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.},
Eprint = {1703.04627},
Journal = {ArXiv e-prints},
Keywords = {Astrophysics - Astrophysics of Galaxies},
Month = mar,
Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}},
Year = 2017}

@book{Binney:2008,
Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
Adsurl = {http://adsabs.harvard.edu/abs/2008gady.book.....B},
Author = {{Binney}, J. and {Tremaine}, S.},
Booktitle = {Galactic Dynamics: Second Edition, by James Binney and Scott Tremaine.~ISBN 978-0-691-13026-2 (HB).~Published by Princeton University Press, Princeton, NJ USA, 2008.},
Publisher = {Princeton University Press},
Title = {{Galactic Dynamics: Second Edition}},
Year = 2008}

@article{zenodo,
Abstractnote = {<p>Gala is a Python package for Galactic astronomy and gravitational dynamics. The bulk of the package centers around implementations of gravitational potentials, numerical integration, and nonlinear dynamics.</p>},
Author = {Adrian Price-Whelan and Brigitta Sipocz and Syrtis Major and Semyeong Oh},
Date-Modified = {2017-08-13 14:14:18 +0000},
Doi = {10.5281/zenodo.833339},
Month = {Jul},
Publisher = {Zenodo},
Title = {adrn/gala: v0.2.1},
Year = {2017},
Bdsk-Url-1 = {http://dx.doi.org/10.5281/zenodo.833339}}

@ARTICLE{gaia,
author = {{Gaia Collaboration} and {Prusti}, T. and {de Bruijne}, J.~H.~J. and
{Brown}, A.~G.~A. and {Vallenari}, A. and {Babusiaux}, C. and
{Bailer-Jones}, C.~A.~L. and {Bastian}, U. and {Biermann}, M. and
{Evans}, D.~W. and et al.},
title = "{The Gaia mission}",
journal = {\aap},
archivePrefix = "arXiv",
eprint = {1609.04153},
primaryClass = "astro-ph.IM",
keywords = {space vehicles: instruments, Galaxy: structure, astrometry, parallaxes, proper motions, telescopes},
year = 2016,
month = nov,
volume = 595,
eid = {A1},
pages = {A1},
doi = {10.1051/0004-6361/201629272},
adsurl = {http://adsabs.harvard.edu/abs/2016A%26A...595A...1G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{astropy,
author = {{Astropy Collaboration} and {Robitaille}, T.~P. and {Tollerud}, E.~J. and
{Greenfield}, P. and {Droettboom}, M. and {Bray}, E. and {Aldcroft}, T. and
{Davis}, M. and {Ginsburg}, A. and {Price-Whelan}, A.~M. and
{Kerzendorf}, W.~E. and {Conley}, A. and {Crighton}, N. and
{Barbary}, K. and {Muna}, D. and {Ferguson}, H. and {Grollier}, F. and
{Parikh}, M.~M. and {Nair}, P.~H. and {Unther}, H.~M. and {Deil}, C. and
{Woillez}, J. and {Conseil}, S. and {Kramer}, R. and {Turner}, J.~E.~H. and
{Singer}, L. and {Fox}, R. and {Weaver}, B.~A. and {Zabalza}, V. and
{Edwards}, Z.~I. and {Azalee Bostroem}, K. and {Burke}, D.~J. and
{Casey}, A.~R. and {Crawford}, S.~M. and {Dencheva}, N. and
{Ely}, J. and {Jenness}, T. and {Labrie}, K. and {Lim}, P.~L. and
{Pierfederici}, F. and {Pontzen}, A. and {Ptak}, A. and {Refsdal}, B. and
{Servillat}, M. and {Streicher}, O.},
title = "{Astropy: A community Python package for astronomy}",
journal = {\aap},
archivePrefix = "arXiv",
eprint = {1307.6212},
primaryClass = "astro-ph.IM",
keywords = {methods: data analysis, methods: miscellaneous, virtual observatory tools},
year = 2013,
month = oct,
volume = 558,
eid = {A33},
pages = {A33},
doi = {10.1051/0004-6361/201322068},
adsurl = {http://adsabs.harvard.edu/abs/2013A%26A...558A..33A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{Aghabozorgi2015,
abstract = {Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works.},
author = {Saeed Aghabozorgi and Ali Seyed Shirkhorshidi and Teh Ying Wah},
Expand Down Expand Up @@ -224,3 +71,73 @@ @misc{UCRArchive2018
month = {October},
note = {\url{https://www.cs.ucr.edu/~eamonn/time_series_data_2018/}}
}

@article{Sakoe1978,
abstract = {This paper reports on an optimum dynamic programming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm superiority is established. A new technique, called slope constraint, is successfully introduced, in which the warping function slope is restricted so as to improve discrimination between words in different categories. The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimental comparison with various DP-algorithms, previously applied to spoken word recognition by different research groups. The experiment shows that the present algorithm gives no more than about two-thirds errors, even compared to the best conventional algorithm. © 1978 IEEE},
author = {Hiroaki Sakoe and Seibi Chiba},
doi = {10.1109/TASSP.1978.1163055},
issn = {00963518},
issue = {1},
journal = {IEEE Transactions on Acoustics, Speech, and Signal Processing},
pages = {43-49},
title = {Dynamic Programming Algorithm Optimization for Spoken Word Recognition},
volume = {26},
year = {1978},
}

@article{Rajabi2020,
abstract = {Smart meters have been widely deployed in power networks since the last decade. This trend has resulted in an enormous volume of data being collected from the electricity customers. To gain benefits for various stakeholders in power systems, proper data mining techniques, such as clustering, need to be employed to extract the underlying patterns from energy consumptions. In this paper, a comparative study of different techniques for load pattern clustering is carried out. Different parameters of the methods that affect the clustering results are evaluated and the clustering algorithms are compared for two data sets. In addition, the two suitable and commonly used data size reduction techniques and feature definition/extraction methods for load pattern clustering are analysed. Furthermore, the existing studies on clustering of electricity customers are reviewed and the main results are highlighted. Finally, the future trends and major applications of clustering consumption patterns are outlined to inform industry practitioners and academic researchers to optimize smart meter operational use and effectiveness.},
author = {Amin Rajabi and Mohsen Eskandari and Mojtaba Jabbari Ghadi and Li Li and Jiangfeng Zhang and Pierluigi Siano},
doi = {10.1016/j.rser.2019.109628},
issn = {18790690},
journal = {Renewable and Sustainable Energy Reviews},
keywords = {Clustering algorithms,Comparative study,Data mining,Load pattern,Smart grids,Smart meters},
month = {3},
publisher = {Elsevier Ltd},
title = {A comparative study of clustering techniques for electrical load pattern segmentation},
volume = {120},
year = {2020},
}

@misc{Tavenard2020,
abstract = {tslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. It follows scikit-learn's Application Programming Interface for transformers and estimators, allowing the use of standard pipelines and model selection tools on top of tslearn objects. It is distributed under the BSD-2-Clause license, and its source code is available at https://github.com/tslearn-team/tslearn.},
author = {Romain Tavenard and Johann Faouzi and Gilles Vandewiele and Felix Divo and Guillaume Androz and Chester Holtz and Marie Payne and Roman Yurchak and Marc Rußwurm},
journal = {Journal of Machine Learning Research},
keywords = {classification,clustering,data mining,pre-processing,time series},
pages = {1-6},
title = {Tslearn, A Machine Learning Toolkit for Time Series Data},
volume = {21},
url = {https://github.com/tslearn-team/tslearn.},
year = {2020},
}

@misc{Dau2018,
author = {Hoang Anh Dau and Eamonn Keogh and Kaveh Kamgar and Chin-Chia Michael Yeh and Yan Zhu and Shaghayegh Gharghabi and Chotirat Ann Ratanamahatana and Yanping and Bing Hu and Nurjahan Begum and Anthony Bagnall and Abdullah Mueen and Gustavo Batista},
month = {10},
title = {The UCR Time Series Classification Archive},
url = {https://www.cs.ucr.edu/~eamonn/time_series_data_2018/},
year = {2018},
}

@article{Huangfu2018,
abstract = {This paper introduces the design and implementation of two parallel dual simplex solvers for general large scale sparse linear programming problems. One approach, called PAMI, extends a relatively unknown pivoting strategy called suboptimization and exploits parallelism across multiple iterations. The other, called SIP, exploits purely single iteration parallelism by overlapping computational components when possible. Computational results show that the performance of PAMI is superior to that of the leading open-source simplex solver, and that SIP complements PAMI in achieving speedup when PAMI results in slowdown. One of the authors has implemented the techniques underlying PAMI within the FICO Xpress simplex solver and this paper presents computational results demonstrating their value. In developing the first parallel revised simplex solver of general utility, this work represents a significant achievement in computational optimization.},
author = {Q. Huangfu and J. A.J. Hall},
doi = {10.1007/s12532-017-0130-5},
issn = {18672957},
issue = {1},
journal = {Mathematical Programming Computation},
keywords = {Linear programming,Parallel computing,Revised simplex method},
month = {3},
pages = {119-142},
publisher = {Springer Verlag},
title = {Parallelizing the dual revised simplex method},
volume = {10},
year = {2018},
}

@misc{gurobi,
author = {{Gurobi Optimization, LLC}},
title = {{Gurobi Optimizer Reference Manual}},
year = 2023,
url = "https://www.gurobi.com"
}
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