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Activity Number: 630 - Uncertainty Quantification, Reliability and Robust Inference
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Defense and National Security
Abstract #330289 Presentation
Title: Visualizing Clustering and Uncertainty Analysis of Multivariate Time-Series Data
Author(s): Kristin Divis* and Maximillian Chen and Laura A McNamara and Dan Morrow
Companies: Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories
Keywords: multivariate time-series data; probabilistic clustering models; Hidden Markov Model; visualization; uncertainty quantification

Multivariate time-series datasets are intrinsic to the study of dynamic, naturalistic behavior, such as in the applications of finance and motion video analysis. Statistical models provide the ability to identify event patterns in these data under conditions of uncertainty, but researchers must be able to evaluate how well a model uses available information in a dataset for clustering decisions and for uncertainty information. The Hidden Markov Model (HMM) is an established method for clustering time-series data, where the hidden states of the HMM are the clusters. We develop novel methods for quantifying the uncertainty of the performance of and for visualizing the clustering performance and uncertainty of fitting a HMM to multivariate time-series data. We explain the usefulness of uncertainty quantification and visualization with evaluating the performance of clustering models, as well as how information exploitation of time-series datasets can be enhanced. Our methods are implemented on simulated data, as well as eyetracking data, with an emphasis on identifying eye movement types (e.g. fixation) and clustering patterns of scanpaths from the raw eye tracking data.

Authors who are presenting talks have a * after their name.

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