time-series multivariate model
Multivariate Bayesian Structural Time Series Model
... in time series ...Structural Time Series (BSTS) model, a technique that can be used for feature selection, time series forecasting, nowcasting, inferring causal ...
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Specification Testing for Multivariate Time Series Volatility Models
... volatility model with i.i.d. innovations is called a strong form volatility model in the literature ...volatility model is correctly specified while the standardized innovation displays higher order ...
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Semiparametric Estimation in Multivariate Nonstationary Time Series Models
... The paper is organized as follows. Section 2 proposes estimators of the parameter matrix A and the nonlinear functions g(·). Section 3.1 establishes that the proposed semiparametric least squares (SLS) estimator of A ...
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Copula-based semiparametric models for multivariate time series
... in time series modeling. Copulas can be used either to model the dependence between successive values of a univariate time series, or to model the conditional dependence of a ...
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On the Efficiency of Outlier Generating Mechanisms in Multivariate Time Series
... in multivariate time ...Innovative model was able to detect 5 but Multiplicative model detected no outlier as a result of non-multiplicative nature of ...
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Optimization in semi-supervised classification of multivariate time series
... The model cannot separate the desirable behavior from the undesirable but rather the normal behavior from the previously unseen ...The model in the setting of this thesis needs a method for telling whether ...
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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
... In this monograph, we began by discussing Bayesian VAR modelling, before arguing that it might be desirable to allow the VAR coe¢ cients to vary over time. This led us to the homoskedastic TVP-VAR. Next, we argued ...
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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
... a model is a useful one since it allows for change in VAR coe¢cients over time (which is potentially of great empirical importance), but also helps avoid over-parameterization problems by allowing for some ...
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A similarity of multivariate time series in stocks network analysis
... The similarity between stocks is customarily quantified by using Pearson correlation coefficient (PCC) on the time series of logarithmic closing price returns. For studying the correlations of stocks, the ...
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Locally Adaptive Factor Processes for Multivariate Time Series
... (BCR) model, which defines the covariance matrix as a regularized quadratic function of time- varying loadings in a latent factor model, characterizing the latter as a sparse combination of a ...
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Goodness-of-Fit Tests for Copulas of Multivariate Time Series
... model with diagonal stochastic volatility matrices, Chen and Fan [10] showed the remarkable result that estimating the copula parameters using the rank-based maximum pseudo-likelihood method [11,12] with the ranks ...
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An Approach Of Urban Expansion Prediction Using Svm And Var
... a multivariate time series forecasting algorithm, is also used to forecast the growth of variables in ...This model forecasts growth of those variables using the data labeled as built-up by ...
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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
... economist will potentially have dozens or hundreds of time series variables to work with. Especially when forecasting, the researcher wants to include as much information as possible, and it can be ...
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Inputs Selection for Artificial Neural Networks for Multivariate time Series
... stochastic model, q(B), f(B) are the moving average and autoregressive operators respectively for n t ...function model requires a considerable amount of computation to identify the model, estimate ...
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Time Series Modeling of Irregularly Sampled Multivariate Clinical Data
... “appropriate” model from a pool of candidates to predict patient’s specific clinical ...Bayesian model averaging for each individual patient where the prediction is made by first searching for models having ...
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Meta-heuristic Methods for Outliers Detection in Multivariate Time Series
... explicit model (parametric approach) from those that use non-explicit models (nonparametric ...autoregressive model, (Chang and Tiao, 1983; Chang et ...(ARIMA) model and proposed an iter- ative ...
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Temporal aggregation of univariate and multivariate time series models: A survey
... Granger (1969) formally introduces the concept of spurious instantaneous causality, meaning instan- taneous causality between variables observed at the low frequency without any causality at the high frequency. See also ...
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An Empirical Comparison of Distance Measures for Multivariate Time Series Clustering
... on time series similarity measures have become ...the time series data ...Dynamic Time Warping (DTW) [12, 13], Weighted DTW (WDTW) [2], Longest Common Subsequence (LCSS) [14], Edit ...
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Great Power, Arms, And Alliances
... a time series multivariate regression model using country year dyads between the major exporting states and all of the other states in the international system is developed to look at how ...
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A multiplex network approach for the analysis of intracranial pressure and heart rate data in traumatic brain injured patients
... price time series (Long 2013), to the detection of sequential motifs in visibility graphs (Iacovacci and Lacasa ...of multivariate time series, as proposed in (Lacasa et ...a ...
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