
% A

@article{alonso2019clustering,
  title={Clustering time series by linear dependency},
  author={Alonso, Andr{\'e}s M and Pena, Daniel},
  journal={Statistics and Computing},
  volume={29},
  number={4},
  pages={655--676},
  year={2019},
  publisher={Springer}
}

% B

@article{bagnall2018uea,
  title={The UEA multivariate time series classification archive, 2018},
  author={Bagnall, Anthony and Dau, Hoang Anh and Lines, Jason and Flynn, Michael and Large, James and Bostrom, Aaron and Southam, Paul and Keogh, Eamonn},
  journal={arXiv preprint arXiv:1811.00075},
  year={2018}
}

@article{bagnallweb,
  title={The UEA & UCR Time Series Classification Repository},
  author={Bagnall, Anthony and Lines, Jason and Vickers, William and Keogh, Eamonn},
  journal={www.timeseriesclassification.com},
  year={2022}
}

% D

@article{d2009autocorrelation,
  title={Autocorrelation-based fuzzy clustering of time series},
  author={D'Urso, Pierpaolo and Maharaj, Elizabeth Ann},
  journal={Fuzzy Sets and Systems},
  volume={160},
  number={24},
  pages={3565--3589},
  year={2009},
  publisher={Elsevier}
}

@article{d2012wavelets,
  title={Wavelets-based clustering of multivariate time series},
  author={D'Urso, Pierpaolo and Maharaj, Elizabeth Ann},
  journal={Fuzzy Sets and Systems},
  volume={193},
  pages={33--61},
  year={2012},
  publisher={Elsevier}
}

% E

@inproceedings{egri2017cross,
  title={Cross-correlation based clustering and dimension reduction of multivariate time series},
  author={Egri, Attila and Horv{\'a}th, Ill{\'e}s and Kov{\'a}cs, Ferenc and Molontay, Roland and Varga, Kriszti{\'a}n},
  booktitle={2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)},
  pages={000241--000246},
  year={2017},
  organization={IEEE}
}

% H

@article{he2018unsupervised,
  title={Unsupervised classification of multivariate time series using VPCA and fuzzy clustering with spatial weighted matrix distance},
  author={He, Hong and Tan, Yonghong},
  journal={IEEE transactions on cybernetics},
  volume={50},
  number={3},
  pages={1096--1105},
  year={2018},
  publisher={IEEE}
}

@inproceedings{hyndman2015large,
  title={Large-scale unusual time series detection},
  author={Hyndman, Rob J and Wang, Earo and Laptev, Nikolay},
  booktitle={2015 IEEE international conference on data mining workshop (ICDMW)},
  pages={1616--1619},
  year={2015},
  organization={IEEE}
}

% K

@article{kakizawa1998discrimination,
  title={Discrimination and clustering for multivariate time series},
  author={Kakizawa, Yoshihide and Shumway, Robert H and Taniguchi, Masanobu},
  journal={Journal of the American Statistical Association},
  volume={93},
  number={441},
  pages={328--340},
  year={1998},
  publisher={Taylor \& Francis}
}

% L

@article{lafuente2016clustering,
  title={Clustering of time series using quantile autocovariances},
  author={Lafuente-Rego, Borja and Vilar, Jos{\'e} A},
  journal={Advances in Data Analysis and classification},
  volume={10},
  number={3},
  pages={391--415},
  year={2016},
  publisher={Springer}
}

@article{li2019multivariate,
  title={Multivariate time series clustering based on common principal component analysis},
  author={Li, Hailin},
  journal={Neurocomputing},
  volume={349},
  pages={239--247},
  year={2019},
  publisher={Elsevier}
}

@article{lopez2021f4,
  title={F4: An All-Purpose Tool for Multivariate Time Series Classification},
  author={Lopez-Oriona, Angel and Vilar, Jose A},
  journal={Mathematics},
  volume={9},
  number={23},
  pages={3051},
  year={2021},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@article{lopez2021quantile,
  title={Quantile cross-spectral density: A novel and effective tool for clustering multivariate time series},
  author={Lopez-Oriona, Angel and Vilar, Jose A},
  journal={Expert Systems with Applications},
  volume={185},
  pages={115677},
  year={2021},
  publisher={Elsevier}
}

@article{lopez2021outlier,
  title={Outlier detection for multivariate time series: A functional data approach},
  author={Lopez-Oriona, Angel and Vilar, Jose A},
  journal={Knowledge-Based Systems},
  volume={233},
  pages={107527},
  year={2021},
  publisher={Elsevier}
}

% M

@article{maharaj1999comparison,
  title={Comparison and classification of stationary multivariate time series},
  author={Maharaj, Elizabeth Ann},
  journal={Pattern Recognition},
  volume={32},
  number={7},
  pages={1129--1138},
  year={1999},
  publisher={Elsevier}
}

@article{mei2015learning,
  title={Learning a mahalanobis distance-based dynamic time warping measure for multivariate time series classification},
  author={Mei, Jiangyuan and Liu, Meizhu and Wang, Yuan-Fang and Gao, Huijun},
  journal={IEEE transactions on Cybernetics},
  volume={46},
  number={6},
  pages={1363--1374},
  year={2015},
  publisher={IEEE}
}

% P

@article{piccolo1990distance,
  title={A distance measure for classifying ARIMA models},
  author={Piccolo, Domenico},
  journal={Journal of time series analysis},
  volume={11},
  number={2},
  pages={153--164},
  year={1990},
  publisher={Wiley Online Library}
}

% R

@article{ruiz2021great,
  title={The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances},
  author={Ruiz, Alejandro Pasos and Flynn, Michael and Large, James and Middlehurst, Matthew and Bagnall, Anthony},
  journal={Data Mining and Knowledge Discovery},
  volume={35},
  number={2},
  pages={401--449},
  year={2021},
  publisher={Springer}
}

% S

@article{singhal2005clustering,
  title={Clustering multivariate time-series data},
  author={Singhal, Ashish and Seborg, Dale E},
  journal={Journal of Chemometrics: A Journal of the Chemometrics Society},
  volume={19},
  number={8},
  pages={427--438},
  year={2005},
  publisher={Wiley Online Library}
}

@article{shokoohi2017generalizing,
  title={Generalizing DTW to the multi-dimensional case requires an adaptive approach},
  author={Shokoohi-Yekta, Mohammad and Hu, Bing and Jin, Hongxia and Wang, Jun and Keogh, Eamonn},
  journal={Data mining and knowledge discovery},
  volume={31},
  number={1},
  pages={1--31},
  year={2017},
  publisher={Springer}
}

% W

@article{wan2022dimensionality,
  title={Dimensionality reduction for multivariate time-series data mining},
  author={Wan, Xiaoji and Li, Hailin and Zhang, Liping and Wu, Yenchun Jim},
  journal={The Journal of Supercomputing},
  volume={78},
  number={7},
  pages={9862--9878},
  year={2022},
  publisher={Springer}
}

@inproceedings{wang2007structure,
  title={Structure-based statistical features and multivariate time series clustering},
  author={Wang, Xiaozhe and Wirth, Anthony and Wang, Liang},
  booktitle={Seventh IEEE international conference on data mining (ICDM 2007)},
  pages={351--360},
  year={2007},
  organization={IEEE}
}

@article{weng2008classification,
  title={Classification of multivariate time series using two-dimensional singular value decomposition},
  author={Weng, Xiaoqing and Shen, Junyi},
  journal={Knowledge-Based Systems},
  volume={21},
  number={7},
  pages={535--539},
  year={2008},
  publisher={Elsevier}
}

@article{weng2008classificationlpp,
  title={Classification of multivariate time series using locality preserving projections},
  author={Weng, Xiaoqing and Shen, Junyi},
  journal={Knowledge-Based Systems},
  volume={21},
  number={7},
  pages={581--587},
  year={2008},
  publisher={Elsevier}
}

% Y

@inproceedings{yang2004pca,
  title={A PCA-based similarity measure for multivariate time series},
  author={Yang, Kiyoung and Shahabi, Cyrus},
  booktitle={Proceedings of the 2nd ACM international workshop on Multimedia databases},
  pages={65--74},
  year={2004}
}

% Z

@inproceedings{zagorecki2015versatile,
  title={A versatile approach to classification of multivariate time series data},
  author={Zagorecki, Adam},
  booktitle={2015 Federated Conference on Computer Science and Information Systems (FedCSIS)},
  pages={407--410},
  year={2015},
  organization={IEEE}
}
