observations of the magnetosphere collected by the Cassini spacecraft in orbit around Saturn.. ..in this case, the best-performing method was the Matrix Profile.. Kiri L. Wagstaf et. al. NASA JPL.2020
(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).
The MatrixProfile technique  is the state-of-the-art anomaly detection technique for continuous time series.  Bart Goethals et. al. (ECML-PKDD 2019).
Based on the concept of Matrix Profile ..without relying on time series synchronization.. the Railway Technologies Laboratory of Virginia Tech has been developing an automated onboard data analysis for the maintenance track system  Ahmadian et. al. JRC2019
Matrix Profile is the state-of-the-art similarity-based outlier detection method.  Christian Jensen et. al. IJCAI-19
we use the exact method based on the Matrix Profile (to assess the effectiveness of therapy) Funkner et al Procedia 2019.
Recently, a research group from UCR have proposed a powerful tool - the Matrix Profile (MP) as a primitive...(we use it for) fault detection Jing Zhang et al. ICPHM 2019
Inspecting both graphs one can see that the matrix-profile algorithm was able to identify regions where there is a  change on the power level over the observed band. F Lobao 2019.
RAMP builds upon an existing time series data analysis technique called Matrix Profile to detect anomalous distances...collected from scientific workflows in an online manner. Herath et. al. IEEE Big Data 2019
Based on obtained results for the considered data set, matrix profiles turned out to be most suitable for the task of anomaly detection Lohfink et al. VISSEC2019
The computation speed and exactness of the Matrix Profile make it a powerful tool and (our) results back this. Barry & Crane AICS 2019
(examining) manufacturing batches considering raw amperage (we found that the) Matrix Profile highlights anomalies Hillion & O'Connell of TIBCO Data Science. re:Invent 2019. [video]
we use the exact method based on the matrix profile to search for motifs (that) can be used to monitor the patient's condition, to assess the effectiveness of therapy or to assess the physician's actions. Funknera et al. YSCCS 2019
(The Matrix Profile is a) similarity join to measure the similarity between two given sequences. we opt for the median of the profile array as the representative distance (3D Dancing Move Synthesis from Music)" Anh et al. IEEE Robotics and Automation Letters (video)
We were amazed by the power of MP and seek to incorporate it into our framework Ye and Ageno.
..adopting the concept of (the) Matrix Profile, we conduct the first attempt to.. J. Zuo et. al. Big Data 20019
The accuracies obtained ...indicate that the Matrix Profile is useful for the task at hand instead of using the CNN features directly  Dhruv Batheja
To speed up online bad PMU data detection a fast discovery strategy is introduced based on (the Matrix Profile)  Zhu and Hill.
Specifically, ALDI uses the matrix profile method to quantify the similarities of daily subsequences in time series meter data, Zoltan Nagy, Energy & Buildings (2020)
Our two-fold approach first leverages the Matrix Profile technique for time series data mining.. Nichiforov 2020.
 the class of matrix profile algorithms.. ..is a promising approach, as it allows simplified post-processing and analysis steps by examining the resulting matrix profile structureA. Raoofy et al.
We only require information about the time of several critical incidents to train our methods, as previously. To this end, we employ the Matrix Profle.. Bellas. et al.
a matrix-profile based algorithm applied across all trajectory data against a validation set revealed four significant motifs which we defined as motif A, B, C and D..  Fernandez Alvarez 2020.
The main building block of this (game analytics) algorithm is the matrix profile, Saadat and Sukthankar AAAI2020

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.