LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses
Juniper L. Simonis, Erica M. Christensen, David J. Harris, Renata M. Diaz, Hao Ye, Ethan P. White, S. K. Morgan Ernest · 2019
Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) , Western and Kleykamp (2004) , Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) .