Hands-On Time-Series Analysis with Matlab

ICDM 2006, Hong-Kong


Michalis Vlachos, Spiros Papadimitriou

IBM T.J. Watson Research Center, Hawthorne, NY, 10532

Tutorial Homepage: http://www.cs.ucr.edu/~mvlachos/ICDM06



Time-series are probably the most prevalent form of data storage and representation in most scientific fields. Examples include industrial or environmental measurements, medical monitoring, stock market analysis, etc. However, in order to efficiently search and explore the ever-increasing amount of collected data, one needs to deploy intelligent techniques for data compression/representation, data organization/pruning and similarity characterization. This tutorial will provide a unified, geometric view of data representation techniques.  Furthermore, it will demonstrate how the above tasks can be performed within the environment of the Matlab programming language and software tool, which is easily accessible in many academic institutions.


The tutorial consists of three parts. The first part covers the basics of the Matlab programming language and environment. The second parts provides the basic mathematical tools for time-series representation and analysis. The third part demonstrates how to use Matlab in order to accomplish various time-series analysis and matching techniques, covering a variety of rudimentary and advanced methods. The most influential and state-of-the-art techniques from the most recent data-mining/database conferences will also be explained. Topics that will be addressed include:


  • Time-Series representations (Fourier, Wavelet, SVD, Symbolic)
  • Distance Functions and Lower Bounding (Euclidean, Time-Warping)
  • Clustering/Classification/Visualization (NN, Dendograms, kMeans, etc)
  • Test Cases and Applications


By the end of the tutorial the attendees will have a basic understanding of the Matlab language, and how it could be applied for solving various time-series analysis and matching problems.




The goal of this tutorial is to convey basic and advanced time-series/data-mining techniques to its audience. Therefore, this tutorial addresses a wide audience such as:

  • Graduate and Undergraduate Students

  • Data Mining Researchers/Educators

  • Industry Developers


Tutorial Material


Slides: [ppt] [pdf]  

Supporting videos: [zip]

Origins of Matlab video: [link]


Disclaimer: If you find the tutorial material useful, feel free to use them for educational purposes, but remember to acknowledge the source!


Sample Slides