Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure
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.
Cite
Text
Listgarten et al. "Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure." Neural Information Processing Systems, 2006.Markdown
[Listgarten et al. "Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/listgarten2006neurips-bayesian/)BibTeX
@inproceedings{listgarten2006neurips-bayesian,
title = {{Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure}},
author = {Listgarten, Jennifer and Neal, Radford M. and Roweis, Sam T. and Puckrin, Rachel and Cutler, Sean},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {905-912},
url = {https://mlanthology.org/neurips/2006/listgarten2006neurips-bayesian/}
}