The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and ﬁnds latent variables. The idea is to discover hidden variables and learn their dynamics, making our algorithm able to function even when there are missing values. We performed experiments on both real and synthetic datasets spanning several megabytes, including motion capture sequences and chlorine levels in drinking water. We show that our proposed DynaMMo method (a) can successfully learn the latent variables and their evolution; (b) can provide high compression for little loss of reconstruction accuracy; (c) can extract compact but powerful features for segmentation, interpretation, and forecasting; (d) has complexity linear on the duration of sequences.

Keywords

Time Series; Missing Value; Bayesian Network; Expectation Maximization (EM)

Notes

Text Reference

Junlei Li, James McCann, Nancy Pollard, and Christos Faloutsos, "DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values," ACM SIGKDD, June/July 2009, pp 527--534, , June, 2009

BibTeX Reference

@article{Li_2009_7276,
   author = "Junlei Li and James McCann and Nancy Pollard and Christos Faloutsos",
   title = "DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values",
   journal = "ACM SIGKDD, June/July 2009, pp 527--534",
   month = "June",
   year = "2009",
   number= "CMU-RI-TR-",
}

The Robotics Institute

Menu