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...

We introduce and analyze Discriminative State-Space Models for forecasting nonstationary time series. We provide data-dependent generalization guarantees for learning these models based on the recently introduced notion of discrepancy. We provide an in-depth analysis of the complexity of such models. We also study the generalization guarantees for several structural risk minimization approaches...

Background: The Multi state Markov models have extensively application with categorization of laboratory marker of CD4 cells for evaluation of HIV disease progression. These models with different states result in different effects of covariates and prediction of HIV disease trend. The main purpose of this study was comparison of four and five states models with the three- state in order to sele...

The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is...

The dynamichazard package implements state space models that can provide a computationally efficient way to model time-varying parameters in survival analysis. I cover the and some of estimation methods implemented dynamichazard, apply them large data set, perform simulation study illustrate methods' computation time performance. One is compared with other R which allow for left-truncation, rig...