Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data

By Satu Helske, Jouni Helske and Mervi Eerola in Hidden Markov models

January 1, 2018

PDF

Abstract

Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an empirical application to life course data but the proposed approach can be useful in various longitudinal problems.

Posted on:
January 1, 2018
Length:
1 minute read, 115 words
Categories:
Hidden Markov models
Tags:
Life course data
See Also:
From sequences to variables - Rethinking the relationship between sequences and outcomes
Analysing Complex Life Sequence Data with Hidden Markov Modelling
Minimum description length based hidden Markov model clustering for life sequence analysis