"(for an industrial IoT problem) Matrix Profiles perform well with almost no parameterisation needed." Anton et al ICDM 2018.
"While there will never be a mathematical silver bullet, we have discovered that the Matrix Profile, a novel algorithm developed by the Keogh research group at UC-Riverside, is a powerful tool." (full post). Andrew Van Benschoten, lead engineer at Target.
" If anybody has ever asked you to analyze time series data and to look for new insights then (the Matrix Profile) is definitely the open source tool that you'll want to add to your arsenal" Sean Law, Ameritrade. NABD 2019.
"(for) intrusion detection in industrial network traffic, distances as calculated with Matrix Profiles rises significantly during the attacks. ..as a result, time series-based anomaly detection methods are capable of detecting deviations and anomalies."  Schotten (2019).

1) Given only the Matrix Profile, most time series data mining tasks are trivial.
2) The Matrix Profile can be computed very efficiently.
3) Algorithms that are built on top the Matrix Profile inherit all its desirable properties. For example, we can use the Matrix Profile to find time series motifs. Because the Matrix Profile can be incrementally computed, we have the first incremental time series motif discovery algorithm. Because the Matrix Profile can be computed in an anytime fashion, we have the first anytime time series motif discovery algorithm. Because the Matrix Profile can leverage GPUs, we have..

1. It is exact: For motif discovery, discord discovery, time series joins etc., the Matrix Profile based methods provide no false positives or false dismissals.
2. It is simple and parameter-free: In contrast, the more general spatial access method algorithms typically require building and tuning spatial access methods and/or hash function.
3. It is space efficient: Matrix Profile construction algorithms requires an inconsequential space overhead, just linear in the time series length with a small constant factor, allowing massive datasets to be processed in main memory.
4. It allows anytime algorithms: While our exact algorithms are extremely scalable, for extremely large datasets we can compute the Matrix Profile in an anytime fashion, allowing ultra-fast approximate solutions.
5. It is incrementally maintainable: Having computed the Matrix Profile for a dataset, we can incrementally update it very efficiently. In many domains this means we can effectively maintain exact joins/motifs/discords on streaming data forever.
6. It does not require the user to set similarity/distance thresholds: For time series joins, the Matrix Profile provides full joins, eliminating the need to specify a similarity threshold, which is an unintuitive task for time series.
7. It can leverage hardware: Matrix Profile construction is embarrassingly parallelizable, both on multicore processors and in distributed systems.
8. It has time complexity that is constant in subsequence length: This is a very unusual and desirable property; all known time series join/motif/discord algorithms scale poorly as the subsequence length grows. In contrast, we have shown time series joins/motifs with subsequences lengths up to 100,000, at least two orders of magnitude longer than any other work we are aware of.
9. It can be constructed in deterministic time: All join/motif/discord algorithms we are aware of can radically different times to finish on two (even slightly) different datasets. In contrast, given only the length of the time series, we can precisely predict in advance how long it will take to compute the Matrix Profile
10. It can handle missing data: Even in the presence of missing data, we can provide answers which are guaranteed to have no false negatives.