State Space form for UC Models
State Space Models and Inverse FORM - A Review
... to state space models poses several new ...inverse FORM approach to include state space models and compute the uncertainty in the RUL ...inverse FORM along with ...
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Fast estimation methods for time series models in state-space form
... the state-space literature: an STSM with a low signal-to-noise ratio and an AR(2) model with observation errors, ...the state and observation ...
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3.1 State Space Models
... systems, the simulation diagrams are elementary analog computers that solve differential equations describing systems dynamics. They are composed of integrators, adders, subtracters, and multipliers, which are physically ...
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Linear State Space Models
... We can then choose, as state variables, x i (t) = v i (t), which lead to the following state space model for the system. The above model has a special form. We will see later that any ...
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The state space models toolbox for MATLAB
... using state space ...C-implemented state space ...predefined models available for immediate use, any models expressible in state space form, with appropriate ...
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Applications of and extensions to state-space models
... Discussion State-space models, or more generally directed graphical models, provide a powerful and flexible framework for modeling dynamic ...Gaussian state-space model for ...
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Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form
... of models was found to be valuable in modelling a panel of exchange rates, allowing the exchange rates to be dissected into country-specific level and variance ...
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Models for optimising the theta method and their relationship to state space models
... The categorisation of a series as trended or not is based di- rectly on the model form chosen by the ETS algorithm ap- plied to the original data. Table 6 presents the percentage drops in the value of MASE ...
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Models for optimising the theta method and their relationship to state space models
... The categorisation of a series as trended or not is based di- rectly on the model form chosen by the ETS algorithm ap- plied to the original data. Table 6 presents the percentage drops in the value of MASE ...
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From general State-Space to VARMAX models
... 5 Concluding remarks The method described in this paper has several practical uses and some theoretical implications that can be summarized in the following items. First, it transforms a structural SS form into an ...
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State Space Models with Endogenous Regime Switching
... of state space models whose parameters are switching endogenously between two regimes, depending on whether an autoregressive latent factor crosses some threshold ...flexible form of ...
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Generalized Extreme Value Distribution with Time-Dependence Using the AR and MA Models in State Space Form
... three models for the TOPIX minima data; the GEV-AR model, the GEV-MA model and the simple GEV model where φ = θ = 0, labeled GEV, for ...both models, the 95% credible interval of the corresponding parameter ...
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Uncertainty Quantification in Remaining Useful Life of Aerospace Components using State Space Models and Inverse FORM
... to state space models poses several new ...inverse FORM approach to include state space models and compute the uncertainty in the RUL ...inverse FORM along with ...
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State space reparametrization for approximating nonlinear models in Bayesian state estimation
... object state before starting the filtering process, and proper modeling of this initial distribution is essential to obtaining accurate ...the form of the estimated distributions or the used ...the ...
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Prediction Intervals for Exponential Smoothing State Space Models.
... Prediction intervals for the additive Holt-Winters method and the multiplicative Holt-Winters method have previously been considered by Chatfield and Yar. For the additive Holt-Winters method they found an exact formula ...
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On Particle Methods for Parameter Estimation in State-Space Models
... non-Gaussian state-space models are ubiquitous in statistics, econometrics, information engineering and signal ...associated state inference problems. However, in most applications, the ...
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CiteSeerX — Identification of Mixed Linear/Nonlinear State-Space Models
... V. N UMERICAL I LLUSTRATIONS In this section we will evaluate the proposed method on simulated data. Two different examples will be presented, first a linear Gaussian system and thereafter a nonlinear system. The purpose ...
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An Assessment of Alternative State Space Models for Count Time Series
... discrete state space models, one of which drives the parameter of the conditional Poisson distribution via a first-order autoregressive stochastic process, and the other of which ex- presses that ...
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Approximate Bayesian inference methods for stochastic state space models
... factorized form of the variational Bayes algorithm, where the intractable posterior is approximated to factorize to a product of independent ...functional form and parameters of the components are ...
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Linear State Space Models
... The state space model and parameters are the same as for the preceding ...The state space model and parameters are the same as for the preceding exercise, except that the initial condition is ...