  The last decade has seen a flurry of research on all-pairs-similarity-search
(or, self-join) for text, DNA, and a handful of other datatypes, and these
systems have been applied to many diverse data mining problems. Surprisingly,
however, little progress has been made on addressing this problem for time
series subsequences. In this thesis, we have introduced a near universal time
series data mining tool called matrix profile which solves the
all-pairs-similarity-search problem and caches the output in an easy-to-access
fashion. The proposed algorithm is not only parameter-free, exact and scalable,
but also applicable for both single and multidimensional time series. By
building time series data mining methods on top of matrix profile, many time
series data mining tasks (e.g., motif discovery, discord discovery, shapelet
discovery, semantic segmentation, and clustering) can be efficiently solved.
Because the same matrix profile can be shared by a diverse set of time series
data mining methods, matrix profile is versatile and
computed-once-use-many-times data structure. We demonstrate the utility of
matrix profile for many time series data mining problems, including motif
discovery, discord discovery, weakly labeled time series classification, and
representation learning on domains as diverse as seismology, entomology, music
processing, bioinformatics, human activity monitoring, electrical power-demand
monitoring, and medicine. We hope the matrix profile is not the end but the
beginning of many more time series data mining projects.
