Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure

Jennifer Listgarten* , Radford M. Neal* , Sam T. Roweis* , Rachel Puckrin** and Sean Cutler** ,
*Department of Computer Science and **Department of Botany , at the University of Toronto.

To appear in Advances in Neural Information Processing Systems 19, MIT Press, Cambridge, MA, 2007 (NIPS 2006).

(full 8 page paper with colour figures) (pdf)

Abstract: We present a hierarchical Bayesian model for sets of related, but different, classes of time series data. Our model performs alignment simultaneously across all classes, while detecting and characterizing class-specific differences. During inference the model produces, for each class, a distribution over a canonical representation of the class. These class-specific canonical representations are automatically aligned to one another -- preserving common sub-structures, and highlighting differences. We apply our model to compare and contrast solenoid valve current data, and also, liquid-chromatography-ultraviolet-diode array data from a study of the plant Arabidopsis thaliana.


The HPLC data used in this paper is available here, and here is the README file. Please cite this paper when using the data.
Also, here is a link to other related papers.

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