@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 = {

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.

}, 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} } @conference {825, title = {Estimating \iSAX Parameters for Efficiency}, booktitle = {Proceedings of the 27th European Conference on Advances in Databases and Information Systems (ADBIS 2023)}, year = {2023}, abstract = {

\iSAX is considered one of the most efficient indexes for time series. Several parameters affect the construction of an \iSAX index: the sliding window size, the threshold value, the number of segments and the maximum cardinality, the last two being related to the SAX representation. In this paper (i) we consider the effect of each parameter on the efficiency of the \iSAX index, (ii) we evaluate the importance of each parameter, and, (iii) suggest how to optimize these parameters.

}, keywords = {Indexing, Parameter tuning, SAX, Time series}, author = {Tsoukalos, Michalis and Platis, Nikos and Costas Vassilakis} } @conference {829, title = {Estimating \iSAX Parameters for Efficiency}, booktitle = {New Trends in Database and Information Systems (Proceedings of the 2023 European Conference on Advances in Databases and Information Systems / ADBIS 2023)}, year = {2023}, publisher = {Springer Nature Switzerland}, organization = {Springer Nature Switzerland}, address = {Cham}, abstract = {

\iSAX is considered one of the most efficient indexes for time series. Several parameters affect the construction of an \iSAX index: the sliding window size, the threshold value, the number of segments and the maximum cardinality, the last two being related to the SAX representation. In this paper (i) we consider the effect of each parameter on the efficiency of the \iSAX index, (ii) we evaluate the importance of each parameter, and, (iii) suggest how to optimize these parameters.

}, keywords = {Indexing, Parameter tuning, SAX, Time series}, isbn = {978-3-031-42941-5}, doi = {10.1007/978-3-031-42941-5_1}, author = {Tsoukalos, Michalis and Platis, Nikos and Costas Vassilakis} }