Estimating MPdist with SAX and Machine Learning

@InProceedings{10.1007/978-3-031-70421-5_5,
	author="Tsoukalos, Mihalis and Chronis, Pantelis and Platis, Nikos and Vassilakis, Costas",
	editor="Tekli, Joe and Gamper, Johann and Chbeir, Richard and Manolopoulos, Yannis and Sassi, Salma and Ivanovic, Mirjana and Vargas-Solar, Genoveva and Zumpano, Ester",
	title="Estimating MPdist with SAX and Machine Learning",
	booktitle="New Trends in Database and Information Systems",
	year="2025",
	publisher="Springer Nature Switzerland",
	address="Cham",
	pages="46--57",
	abstract="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.",
	isbn="978-3-031-70421-5"
}