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Gaussian Process Conditional Copulas with Applications to Financial Time Series
The estimation of dependencies between multiple variables is a central p...
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On Multivariate Singular Spectrum Analysis
We analyze a variant of multivariate singular spectrum analysis (mSSA), ...
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Estimation of dynamic networks for high-dimensional nonstationary time series
This paper is concerned with the estimation of time-varying networks for...
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On the relationship between beta-Bartlett and Uhlig extended processes
Stochastic volatility processes are used in multivariate time-series ana...
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Correlated functional models with derivative information for modeling MFS data on rock art paintings
Microfading Spectrometry (MFS) is a method for assessing light sensitivi...
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Parsimonious modeling with Information Filtering Networks
We introduce a methodology to construct parsimonious probabilistic model...
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Locally adaptive factor processes for multivariate time series
In modeling multivariate time series, it is important to allow time-vary...
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High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
Predicting the dependencies between observations from multiple time series is critical for applications such as anomaly detection, financial risk management, causal analysis, or demand forecasting. However, the computational and numerical difficulties of estimating time-varying and high-dimensional covariance matrices often limits existing methods to handling at most a few hundred dimensions or requires making strong assumptions on the dependence between series. We propose to combine an RNN-based time series model with a Gaussian copula process output model with a low-rank covariance structure to reduce the computational complexity and handle non-Gaussian marginal distributions. This permits to drastically reduce the number of parameters and consequently allows the modeling of time-varying correlations of thousands of time series. We show on several real-world datasets that our method provides significant accuracy improvements over state-of-the-art baselines and perform an ablation study analyzing the contributions of the different components of our model.
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