Learning Linear Dynamical Systems from Multivariate Time Series: A Matrix Factorization Based Framework.

ABSTRACT: The linear dynamical system (LDS) model is arguably the most commonly used time series model for real-world engineering and financial applications due to its relative simplicity, mathematically predictable behavior, and the fact that exact inference and predictions for the model can be done efficiently. In this work, we propose a new generalized LDS framework, gLDS, for learning LDS models from a collection of multivariate time series (MTS) data based on matrix factorization, which is different from traditional EM learning and spectral learning algorithms. In gLDS, each MTS sequence is factorized as a product of a shared emission matrix and a sequence-specific (hidden) state dynamics, where an individual hidden state sequence is represented with the help of a shared transition matrix. One

SUBMITTER: Liu Z 

PROVIDER: S-EPMC5098501 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature