DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values

Junlei Li, James McCann, Nancy Pollard, and Christos Faloutsos
ACM SIGKDD, June/July 2009, pp 527--534, , June, 2009


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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-",
}