@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/chorochronos/2003,
	title = {Spatio-Temporal Databases: The CHOROCHRONOS Approach},
	volume = {2520},
	year = {2003},
	publisher = {Springer},
	isbn = {3-540-40552-6},
	doi = {10.1007/b83622},
	url = {https://doi.org/10.1007/b83622},
	editor = {Manolis Koubarakis and Timos K. Sellis and Andrew U. Frank and St{\'e}phane Grumbach and Ralf Hartmut G{\"u}ting and Christian S. Jensen and Nikos A. Lorentzos and Yannis Manolopoulos and Enrico Nardelli and Barbara Pernici and Hans-J{\"o}rg Schek and Michel Scholl and Babis Theodoulidis and Nectaria Tryfona}
}
