  The discovery of time series motifs has emerged as one of the most useful
primitives in time series data mining. Researchers have shown its utility for
exploratory data mining, summarization, visualization, segmentation,
classification, clustering, and rule discovery. Although there has been more
than a decade of extensive research, there is still no technique to allow the
discovery of time series motifs in the presence of missing data, despite the
well-documented ubiquity of missing data in scientific, industrial, and medical
datasets. In this work, we introduce a technique for motif discovery in the
presence of missing data. We formally prove that our method is admissible,
producing no false negatives. We also show that our method can piggy-back off
the fastest known motif discovery method with a small constant factor
time/space overhead. We will demonstrate our approach on diverse datasets with
varying amounts of missing data

Machine Learning;Artificial Intelligence;Machine Learning