@conference {838,
	title = {Estimating MPdist with SAX and Machine Learning},
	booktitle = {New Trends in Database and Information Systems (Proceedings of ADBIS 2024, Short Papers, Workshops, Doctoral Consortium and Tutorials)},
	year = {2024},
	publisher = {Springer Cham},
	organization = {Springer Cham},
	abstract = {<p>MPdist is a distance measure which considers two time series to be similar if they share many similar subsequences. However, computing MPdist can be slow, especially for large time series. We propose a technique for the approximate computation of MPdist that uses the SAX representation of the time series to quickly estimate the Nearest Neighbor (NN) distance of each subsequence, and then applies a Machine Learning model to correct the accuracy loss incurred. Our method is orders of magnitude faster than the exact computation of MPdist; at the same time, our best approximation computes the NN of a time series with high accuracy. A thorough evaluation of our technique is provided.</p>
},
	keywords = {Distance metric, Machine Learning, MPdist, Random Forest, SAX, Time series},
	doi = {https://doi.org/10.1007/978-3-031-70421-5_32},
	author = {Mihalis Tsoukalos and Pantelis Chronis and Nikos Platis and Costas Vassilakis},
	editor = {Joe Tekli and Johann Gamper and Richard Chbeir and Yannis Manolopoulos and Salma Sassi and Mirjana Ivanovic and Vargas-Solar, Genoveva and Ester Zumpano}
}
@proceedings {DBLP:conf/ssdbm/2018,
	title = {Proceedings of the 30th International Conference on Scientific and Statistical Database Management, SSDBM 2018, Bozen-Bolzano, Italy, July 09-11, 2018},
	year = {2018},
	publisher = {ACM},
	doi = {10.1145/3221269},
	url = {https://doi.org/10.1145/3221269},
	editor = {Dimitris Sacharidis and Johann Gamper and Michael H. B{\"o}hlen}
}
