Time series data mining is a perennially popular research topic, due to the ubiquity of time series in
medical, financial, industrial, and scientific domains. There are about a dozen major time series data mining
tasks, including:
•    Time Series Motif Discovery
•    Time Series Joins
•    Time Series Classification (shapelet discovery)
•    Time Series Density Estimation
•    Time Series Semantic Segmentation
•    Time Series Visualization
•    Time Series Clustering
•    Time Series Similarity Search (indexing)
•    Time Series Monitoring (complex event processing)
In 2016, an international group of researchers, headed by Dr. Keogh's lab, introduced the Matrix Profile, with the following two surprising claims. Firstly, if you have the Matrix Profile computed, then all time series data mining tasks are easy or trivial, and secondly, computing the Matrix Profile is unexpectedly scalable, and is completely free of the curse of dimensionality. Given these two facts, the Matrix Profile is poised to become an incredibly useful and ubiquitous primitive for time series data mining. It is difficult to overstate the scalability of the Matrix Profile computation, it has been used to perform ten exact quadrillion pairwise comparisons of a single time series during a self-join, surely the largest exact self-join every attempted.

In this talk, we will explain how to use it efficiently to solve problems in time series analytics. The talk will be illustrated with case studies from domains as diverse as entomology, oil-and- gas production, music, bioinformatics. medicine, seismology and human behavior understanding.