| Title | Estimating MPdist with SAX and Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2024 |
| Authors | Tsoukalos M, Chronis P, Platis N, Vassilakis C |
| Editor | Tekli J, Gamper J, Chbeir R, Manolopoulos Y, Sassi S, Ivanovic M, Vargas-Solar G, Zumpano E |
| Conference Name | New Trends in Database and Information Systems (Proceedings of ADBIS 2024, Short Papers, Workshops, Doctoral Consortium and Tutorials) |
| Publisher | Springer Cham |
| Keywords | Distance metric, Machine Learning, MPdist, Random Forest, SAX, Time series |
| 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. |
| DOI | 10.1007/978-3-031-70421-5_32 |
