general state-space models
Fast sequential parameter inference for dynamic state space models
... using general state-space models are examples of such inference ...dynamic state-space models approach. Such models are very well suited for providing a solution to ...
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Forecasting VARMA processes using VAR models and subspace based state space models
... the general design of the experiments and the simulation process, and provide selected simulation results for some VARMA processes: univariate stationary, univariate non- stationary, and bivariate cointegrated; in ...
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Multiplicative State Space Models for Intermittent Time Series
... the last case we usually refer to “intermittent demand” which, in addition to irregularity of occurrence, contains only zeroes and positive values. The final application area is important in a supply-chain setting, where ...
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Using features of models to improve state space exploration
... In our research, machine learning techniques are used to predict a strategy given a set of features of a model. More specifically, the goal is to predict which strategy within a finite set of strategies is the most ...
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Linear State-Space Models for Blind Source Separation
... of general models and estimation schemes; most current work is highly application specific with the majority focused on applications in separation of speech ...
18
Small area estimation with state space models subject to benchmark constraints
... individual State models into a joint model with built in benchmark constrains intensifies the ...the state vector in the separate State models is 30 (see next section), implying that by ...
28
The first stage studies of U state space control system design
... polynomial models. In regarding to using linear state space design approaches for nonlinear polynomial models, there has been a recent research proposing a U-block model [3] defined as a ...
7
When are adaptive expectations rational? A generalization
... more general case, and shows that for a very broad class of time series models–all those that can be written in linear state space form–a gen- eralized form of adaptive expectations is ...
9
A universal U model based control system design
... polynomial models are frequently obtained from principle and/or data driven identification, particularly in the cases of single-input and single-output (SISO) dynamic plants, even though maybe with complex ...
6
Dynamic State Space Models
... dynamic state-space model was developed in the control systems literature, where physi- cal systems are described mathematically as sets of inputs, outputs, and state variables, related by difference ...
81
Monte Carlo fixed-lag smoothing in state-space models
... continuous models of type ...for general diffu- ...high-dimensional models with a drift term that dom- inates, such approximations will be ...
11
Modeling Exchange Rate Dynamics in Egypt: Observed and Unobserved Volatility
... TVP models for the first time on the Egyptian case. These models, although fashionable, have become a standard and popular modeling framework for economic and financial analysis of time ...traditional ...
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Bootstrap Approximation to Prediction MSE for State Space Models with Estimated Parameters
... but general bootstrap method for estimating the Prediction Mean Square Error (PMSE) of the state vector predictors when the unknown model parameters are estimated from the observed ...in ...
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The state space models toolbox for MATLAB
... State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space ...of models, with support for univariate and multivariate models, complex ...
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Modeling Monthly Rainfall Time Series Using Ets State Space And Sarima Models
... ETS state space models are within the limits upon which the models are based, therefore the residuals are white noise suggesting that the models are adequate following (Ramasubramanian, ...
6
Distributional Semantics in Technicolor
... We use one standard dataset (WordSim353) and one new dataset (MEN). WordSim353 (Finkelstein et al., 2002) is a widely used benchmark constructed by asking 16 subjects to rate a set of 353 word pairs on a 10-point ...
10
Efficient State-Space Inference of Periodic Latent Force Models
... In order to develop our LBM for latent forces we shall first investigate current approaches to sparse representations of stationary covariance functions and then demonstrate that one of these approaches, namely the ...
61
Complete synchronization of chaotic atmospheric models by connecting only a subset of state space
... the models show highly non-linear ...fect models synchronize on a common solution that more ac- curately follows the dynamics of the real system than each of the individual models ...
11
Particle Filters and Data Assimilation
... the state process mixes well, then the error at time t − 1 will be reduced when we go forward one time step using a propagation and an update ...of state space models with ergodic dynamics ...
31
PubMedCentral-PMC5378168.pdf
... Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a ...
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