/DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values

DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values

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

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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.

BibTeX Reference
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},
year = {2009},
month = {June},
keywords = {Time Series; Missing Value; Bayesian Network; Expectation Maximization (EM)},