2017/06/19 by Jan Reubold, Reubold, Jan, Thorsten Strufe +3
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Mathematical Dynamics and Fractals #Algorithms and Data Compression
paper · pdf · doi:10.48550/arxiv.1706.06178
We propose a Bayesian nonparametric mixture model for prediction- and information extraction tasks with an efficient inference scheme. It models categorical-valued time series that exhibit dynamics from multiple underlying patterns (e.g. user behavior traces). We simplify the idea of capturing these patterns by hierarchical hidden Markov models (HHMMs) - and extend the existing approaches by the additional representation of structural information. Our empirical results are based on both synthetic- and real world data. They indicate that the results are easily interpretable, and that the model excels at segmentation and prediction performance: it successfully identifies the generating patterns and can be used for effective prediction of future observations.