document: DynaMMo : mining and summarization of coevolving sequences with missing values 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 .
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