FIGURE 1.

Our proposed methodology for time series motifs discovery and summarization. (a) Step 1: Motif extraction using EMD as a similarity metric. The subroutine takes out the most repetitive pattern (possibly with multiple occurrences) of a given time course, (b) Step 2: Summarization of motifs using their probability density computed by a kernel density estimator (KDE). It takes a bag of varying length motifs and generates a concise smaller collection of the most frequent shapes/patterns representing the functional system (SZ/HC). We defined these prototypes as the statelets. The EMD distance matrix is used for both performing the tSNE and computing probability density (PD) of the motifs. The relevant processing blocks and their intuitions in Step 2 are described elaborately in Section 5.2