dynamic time series model
Dynamic and Static Topic Model for Analyzing Time Series Document Collections
... proposed model without the static structure, which we term ...proposed model, DSTM, achieved the smallest PPL, which implies its effectiveness for modeling a collection of technical ...
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Dynamic staged trees for discrete multivariate time series : forecasting, model selection and causal analysis
... steady model has a long history with Dirichlet distributions ...steady model as a justifiable and conjugate method for making inference about tree models whose floret probabilities ...
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A Review: Prognostics and Health Management in Automotive and Aerospace
... mean- time-between-failure (MTBF) ...with time series ...fixed-lag dynamic linear model with an adaptive length moving window for time series fore- casting using a ...
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Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions
... recent time series values (Tversky & Kahneman, 1974) or (b) by contextual adaptation to features of the environment—with steeper trends causing trend-damping and shallower trends leading to anti-damping ...
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Dynamic staged trees for discrete multivariate time series : forecasting, model selection and causal analysis
... graphical model — the dynamic staged tree — is used to model discrete-valued discrete-time multivariate processes which are hypothesised to exhibit certain sym- metries concerning how ...
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A robust Bayesian dynamic linear model for Latin-American economic time series: “the Mexico and Puerto Rico cases”
... Bayesian Dynamic Models (RMBDs) to Latin- American time series from Mexico and Puerto ...the model has the feature of producing not constant credible intervals over time even after ...
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Contagion between United States and european markets during the recent crises
... indexes. Time Series Factor Analysis (TSFA) procedure has allowed concentrating the information of the European indexes into a unique factor, which captures the underlying structure of the European return ...
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The hidden burden of measles in Ethiopia: how distance to hospital shapes the disease mortality rate
... a dynamic transmission model calibrated on the time series of hospitalized measles ...The model provided estimates of disease transmissibility and incidence at a population ...level. ...
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Applying Computational Intelligence Techniques to QoS Time Series Forecasting in Services Computing
... This section discusses the experimental results (i.e., Table 2, Table 3, and Table 4) reported in Section 6.2. We analyze first the training performance and then the testing performance achieved in the experiments. From ...
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A new data driven model for post transplant antibody dynamics in high risk kidney transplantation
... antibody time series and their diverse patterns have made the task of modelling ...post-transplant dynamic pattern with rapid falls and stable settling levels, a novel data-driven model has ...
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Characterizing measles transmission in India: a dynamic modeling study using verbal autopsy data
... Our model is one of the few dynamic models [6, 30] of measles transmission calibrated to measles data from low- and middle-income countries; it is to the best of our knowledge the only such model ...
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Multivariate Bayesian Structural Time Series Model
... our model performs very well in terms of estimation ac- curacy and variables selection ability, even if each target series has a different set of latent states and explanatory variables from ...the ...
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Posterior mean and variance approximation for regression and time series problems
... a model may be partially specified in terms of its first two moments, or its probability distribution may be difficult to specify (or it may be specified with ...for dynamic situation in which a modeller is ...
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Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series
... semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto- regressive ...of ...
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Dynamic Time Series Analysis Using Logistic Function
... For this kind of nonlinear regression model, it is not easy to get a good asymptotic approximation to the finite sample behavior(Granger and Ter¨ asvirta, 1993). Our case also shows the need for a large sample size ...
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Biomathematical Model Study on the Opioid Crisis in America
... And time Series Analysis was used to find the year when these counties reached the drug threshold, and the threshold level was obtained by the dynamic panel data ...
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Effect of Dynamic Time Warping using different Distance Measures on Time Series Classification
... K-Nearest Neighbor (KNN) is an instance-based algorithm that stores all the available inputs and classifies the new input based on a similarity measure. It is also called as a „lazy learner‟ as there is no model ...
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The timing of unemployment response in Austrian regional labour markets The classical and an alternative mode of exploratory statistical analysis
... This model describes a dynamic 'causal' relationship between the two time series and indicates that a change in national unemployment is followed by an asymptotic change in the region's [r] ...
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Comparison Of Fuzzy Time Series And ARIMA Model
... traditional time series and fuzzy time ...new model of max-min composition technique with new fewer complexes then used max-min ...hybrid model was ...fuzzy time series ...
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Model selection for time series of count data
... From Table 1 we observe that the preferred order is p = 2 using the marginal likelihood and p = 3 using the DIC. However, the log marginal likelihood and DIC for p = 2 and p = 3 are close and hence we repeated the ...
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