Finding Repeated Structure in Time Series: Algorithms and Applications
Presenters: Abdullah Mueen, University of New Mexico, USA; Eamonn Keogh, University of California, Riverside, USA
Tutorial Website: http://www.cs.unm.edu/~mueen/Tutorial/SDM2015.html
Abstract
Repeated patterns in time series data are indicative to identical dynamics in the origin. Such patterns can be used to summarize, classify, compress, cluster and classify time series data. In this tutorial, we will present several algorithms for repeated pattern discovery in univariate and multivariate time series data. The algorithms cover a wide range of settings from in-memory to online data, from approximate to exact algorithms and, from one length to all lengths. We will present applications of repeated patterns in several domains and in various data types.
We will cover
- Exact and approximate algorithms for finding repeated patterns in time series
- Clustering, classification and rule discovery algorithms using repeated patterns
- Applications to Entomology, Data center management, Activity recognition
The tutorial will conclude with a list of open problems and research directions.
