PPT-Time Series Data Mining Using the Matrix Profile:
Author : oneill | Published Date : 2023-09-18
A Unifying View of Motif Discovery Anomaly Detection Segmentation Classification Clustering and Similarity Joins Eamonn Keogh Abdullah Mueen We will start at 825am
Presentation Embed Code
Download Presentation
Download Presentation The PPT/PDF document "Time Series Data Mining Using the Matrix..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Time Series Data Mining Using the Matrix Profile:: Transcript
A Unifying View of Motif Discovery Anomaly Detection Segmentation Classification Clustering and Similarity Joins Eamonn Keogh Abdullah Mueen We will start at 825am to allow stragglers to find the room . . Eamonn Keogh . With. Yan Zhu, Chin-. Chia. Michael . Yeh. , Abdullah Mueen. . with contributions from Zachary Zimmerman, Nader . Shakibay. . Senobari. ,, Gareth Funning, Philip Brisk, Liudmila Ulanova, Nurjahan Begum, . . Chin-Chia Michael Yeh, Helga Van Herle, Eamonn Keogh . http://www.cs.ucr.edu/~eamonn/MatrixProfile.html. Outline. Motivation. Proposed method. Experiment result. Conclusion. 2. Outline. Motivation. Yan Zhu, Zachary Zimmerman, Nader . Shakibay. . Senobari. . Chin-Chia Michael Yeh, Gareth Funning, Abdullah Mueen,. Philip Brisk, Eamonn Keogh . http://www.cs.ucr.edu/~eamonn/MatrixProfile.html. 1. 2. : . autocovariance. function of the individual time series . 3. Vector ARMA models. if the roots of the equation. are all greater than 1 in absolute value . Then : infinite MA representation. without. explaining how to compute it!. In this half, we will describe algorithms to compute matrix profile, optimization techniques for scalability, portability to modern hardware, approximation to gain speed, and extension to special cases.. Data . Mining Algorithm. Peter Myers. Bitwise Solutions Pty Ltd. DBI-B326. Presenter Introduction. Peter Myers. BI Expert, Bitwise Solutions Pty Ltd. BBus. , SQL Server MCSE, MCT, SQL Server MVP (since 2007). without. explaining how to compute it!. In this half, we will describe algorithms to compute matrix profile, optimization techniques for scalability, portability to modern hardware, approximation to gain speed, and extension to special cases.. . Eamonn Keogh . With. Yan Zhu, Chin-. Chia. Michael . Yeh. , Abdullah Mueen. . with contributions from Zachary Zimmerman, Nader . Shakibay. . Senobari. ,, Gareth Funning, Philip Brisk, Liudmila Ulanova, Nurjahan Begum, . without. explaining how to compute it!. In this half, we will describe algorithms to compute matrix profile, optimization techniques for scalability, portability to modern hardware, approximation to gain speed, and extension to special cases.. Abdullah Mueen 5 Slides Demo Primitives for Time Series Data Mining Time series motifs Time series shapelets Time series join 0 2000 4000 6000 8000 10000 0 10 20 30 0 100 200 300 400 500 600 700 800 Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one Hundred Million Barrier for Time Series Motifs and Joins Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari Chin-Chia Michael Yeh, Gareth Funning, Abdullah Mueen, Yan Zhu, Zachary Zimmerman, Nader . Shakibay. . Senobari. . Chin-Chia Michael Yeh, Gareth Funning, Abdullah Mueen,. Philip Brisk, Eamonn Keogh . http://www.cs.ucr.edu/~eamonn/MatrixProfile.html. . Chin-Chia Michael Yeh, Helga Van Herle, Eamonn Keogh . http://www.cs.ucr.edu/~eamonn/MatrixProfile.html. Outline. Motivation. Proposed method. Experiment result. Conclusion. 2. Outline. Motivation. Intro. 1. chap2.ppt. 2. April 2. Teleconnections. -EOT. 2a. chap4.ppt. 4. April 2. EOF. 3. chap5.ppt. 5. April 9. Degrees of freedom. 4a. 6. April 9. Constructed Analogue. 4b. chap7.ppt.
Download Document
Here is the link to download the presentation.
"Time Series Data Mining Using the Matrix Profile:"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.
Related Documents
