Hidden Markov gating for prediction of change points in switching dynamical systems
نویسندگان
چکیده
The prediction of switching dynamical systems requires an identi cation of each individual dynamics and an early detection of mode changes. Here we present a uni ed framework of a mixtures of experts architecture and a generalized hidden Markov model (HMM) with a state space dependent transition matrix. The specialization of the experts in the dynamical regimes and the adaptation of the switching probabilities is performed simultaneously during the training procedure. We show that our method allows for a fast on{line detection of mode changes in cases where the most recent input data together with the last dynamical mode contain su cient information to indicate a dynamical change.
منابع مشابه
Hidden Markov gating for prediction ofchange points in switching
The prediction of switching dynamical systems requires an identiication of each individual dynamics and an early detection of mode changes. Here we present a uniied framework of a mixtures of experts architecture and a generalized hidden Markov model (HMM) with a state space dependent transition matrix. The specialization of the experts in the dynamical regimes and the adaptation of the switchi...
متن کاملHidden Markov Mixtures of Experts for Prediction of Non{stationary Dynamics
The prediction of non{stationary dynamical systems may be performed by identifying appropriate sub{dynamics and an early detection of mode changes. In this paper, we present a framework which uniies the mixtures of experts approach and a generalized hidden Markov model with an input{dependent transition matrix: the Hidden Markov Mixtures of Experts (HMME). The gating procedure incorporates stat...
متن کاملVariational Learning for Switching State-Space Models
We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learnsthe parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models -- hidden Markov models and linear dynamical systems -- and is closely related to models that are widely used in ...
متن کاملSwitching State-Space Models
We introduce a statistical model for times series data with nonlinear dynamics which iteratively segments the data into regimes with approximately linear dynamics and learns the parameters of each of those regimes. This model combines and generalizes two of the most widely used stochastic time series models|the hidden Markov model and the linear dynamical system|and is related to models that ar...
متن کاملSegmentation of switching dynamics with a Hidden Markov Model of neural prediction experts
We discuss a framework for modeling the switching dynamics of a time series based on hidden Markov models (HMM) of prediction experts, here neural networks. Learning is treated as a maximum likelihood problem. In particular, we present an Expectation-Maximization (EM) algorithm for adjusting the expert parameters as well as the HMM transition probabilities. Based on this algorithm, we develop a...
متن کامل