Efficient matching of very complex time series

Abstract

We propose a novel method (FANSEA) that performs very complex time series matching. The matching here includes comparison and alignment of time series, for diverse needs: diagnosis, clustering, retrieval, mining, etc. The complexity stands in the fact that the method is able to match quasi-periodic time series, that are eventually phase shifted, of different lengths, composed of different number of periods, characterized by local morphological changes and that might be shifted/scaled on the time/magnitude axis. This is the most complex case that can occur in time series matching. The efficiency stands in the fact that the newly developed FANSEA method produces alignments that are comparable to those of the previously published SEA method. However and as a result of data reduction, FANSEA consumes much less time and data; hence, allowing for faster matching and lower storage space. Basically, FANSEA is composed of two main steps: Data reduction by curve simplification of the time series traces and matching through exchange of extracted signatures between the time series under process. Due to the quasi-periodic nature of the electrocardiogram (ECG), the tests were conducted on records selected from the Massachusetts Institute of Technology-Beth Israel Hospital database (MIT-BIH). Numerically, the new method data reduction was up to 80 % and the time reduction was up to 95 %. Accordingly and among many possible applications, the new method is very suitable for searching, querying and mining of large time series databases.